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autoware_smart_mpc_trajectory_follower package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter autoware_diagnostic_graph_test_examples tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

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Package Summary

Tags	No category tags.
Version	0.47.0
License	Apache License 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description
Checkout URI	https://github.com/autowarefoundation/autoware_universe.git
VCS Type	git
VCS Version	main
Last Updated	2025-08-16
Dev Status	UNKNOWN
Released	UNRELEASED
Tags	planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Nodes to follow a trajectory by generating control commands using smart mpc

Additional Links

No additional links.

Maintainers

Masayuki Aino
Kosuke Takeuchi
Takamasa Horibe
Takayuki Murooka

Authors

Masayuki Aino

control/autoware_smart_mpc_trajectory_follower/README.md

Smart MPC Trajectory Follower

Smart MPC (Model Predictive Control) is a control algorithm that combines model predictive control and machine learning. While inheriting the advantages of model predictive control, it solves its disadvantage of modeling difficulty with a data-driven method using machine learning.

This technology makes it relatively easy to operate model predictive control, which is expensive to implement, as long as an environment for collecting data can be prepared.

</a> ## Provided features This package provides smart MPC logic for path-following control as well as mechanisms for learning and evaluation. These features are described below. ### Trajectory following control based on iLQR/MPPI The control mode can be selected from "ilqr", "mppi", or "mppi_ilqr", and can be set as `mpc_parameter:system:mode` in [mpc_param.yaml](./autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml). In "mppi_ilqr" mode, the initial value of iLQR is given by the MPPI solution. > [!NOTE] > With the default settings, the performance of "mppi" mode is limited due to an insufficient number of samples. This issue is being addressed with ongoing work to introduce GPU support. To perform a simulation, run the following command: ```bash ros2 launch autoware_launch planning_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit trajectory_follower_mode:=smart_mpc_trajectory_follower ``` > [!NOTE] > When running with the nominal model set in [nominal_param.yaml](./autoware_smart_mpc_trajectory_follower/param/nominal_param.yaml), set `trained_model_parameter:control_application:use_trained_model` to `false` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml). To run using the trained model, set `trained_model_parameter:control_application:use_trained_model` to `true`, but the trained model must have been generated according to the following procedure. ### Training of model and reflection in control To obtain training data, start autoware, perform a drive, and record rosbag data with the following commands. ```bash ros2 bag record /localization/kinematic_state /localization/acceleration /vehicle/status/steering_status /control/command/control_cmd /control/trajectory_follower/control_cmd /control/trajectory_follower/lane_departure_checker_node/debug/deviation/lateral /control/trajectory_follower/lane_departure_checker_node/debug/deviation/yaw /system/operation_mode/state /vehicle/status/control_mode /sensing/imu/imu_data /debug_mpc_x_des /debug_mpc_y_des /debug_mpc_v_des /debug_mpc_yaw_des /debug_mpc_acc_des /debug_mpc_steer_des /debug_mpc_X_des_converted /debug_mpc_x_current /debug_mpc_error_prediction /debug_mpc_max_trajectory_err /debug_mpc_emergency_stop_mode /debug_mpc_goal_stop_mode /debug_mpc_total_ctrl_time /debug_mpc_calc_u_opt_time ``` Move [rosbag2.bash](./autoware_smart_mpc_trajectory_follower/training_and_data_check/rosbag2.bash) to the rosbag directory recorded above and execute the following command on the directory ```bash bash rosbag2.bash ``` This converts rosbag data into CSV format for training models. > [!NOTE] > Note that a large number of terminals are automatically opened at runtime, but they are automatically closed after rosbag data conversion is completed. > From the time you begin this process until all terminals are closed, autoware should not be running. Instead, the same result can be obtained by executing the following command in a python environment: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.transform_rosbag_to_csv(rosbag_dir) ``` Here, `rosbag_dir` represents the rosbag directory. At this time, all CSV files in `rosbag_dir` are automatically deleted first. We move on to an explanation of how the model is trained. If `trained_model_parameter:memory_for_training:use_memory_for_training` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml) is set to `true`, training is performed on models that include LSTM, and if it is set to `false`, training is performed on models that do not include LSTM. When using LSTM, cell states and hidden states are updated based on historical time series data and reflected in the prediction. The paths of the rosbag directories used for training and validation, `dir_0`, `dir_1`, `dir_2`,..., `dir_val_0`, `dir_val_1`, `dir_val_2`,... and the directory `save_dir` where you save the models, the model can be saved in the python environment as follows: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.add_data_from_csv(dir_0, add_mode="as_train") model_trainer.add_data_from_csv(dir_1, add_mode="as_train") model_trainer.add_data_from_csv(dir_2, add_mode="as_train") ... model_trainer.add_data_from_csv(dir_val_0, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_1, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_2, add_mode="as_val") ... model_trainer.get_trained_model() model_trainer.save_models(save_dir) ``` If `add_mode` is not specified or validation data is not added, the training data is split to be used for training and validation. After performing the polynomial regression, the NN can be trained on the residuals as follows: ```python model_trainer.get_trained_model(use_polynomial_reg=True) ``` File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/control/autoware_smart_mpc_trajectory_follower/README.md)

CHANGELOG

Changelog for package autoware_smart_mpc_trajectory_follower

0.47.0 (2025-08-11)

feat: change planning output topic name to /planning/trajectory (#11135)
- change planning output topic name to /planning/trajectory
* style(pre-commit): autofix ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Contributors: Yukihiro Saito

0.46.0 (2025-06-20)

0.45.0 (2025-05-22)

Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
fix(smart_mpc): imperative mood (#10658)
Contributors: TaikiYamada4, Yuxuan Liu

0.44.2 (2025-06-10)

0.44.1 (2025-05-01)

0.44.0 (2025-04-18)

0.43.0 (2025-03-21)

Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
Contributors: Hayato Mizushima, Yutaka Kondo

0.42.0 (2025-03-03)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
Contributors: Fumiya Watanabe, 心刚

0.41.2 (2025-02-19)

chore: bump version to 0.41.1 (#10088)
Contributors: Ryohsuke Mitsudome

0.41.1 (2025-02-10)

0.41.0 (2025-01-29)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat: tier4_debug_msgs changed to autoware_internal_debug_msgs in fil… (#9851)
Contributors: Fumiya Watanabe, Vishal Chauhan

0.40.0 (2024-12-12)

Merge branch 'main' into release-0.40.0
Revert "chore(package.xml): bump version to 0.39.0 (#9587)" This reverts commit c9f0f2688c57b0f657f5c1f28f036a970682e7f5.
fix: fix ticket links in CHANGELOG.rst (#9588)
chore(package.xml): bump version to 0.39.0 (#9587)
- chore(package.xml): bump version to 0.39.0
- fix: fix ticket links in CHANGELOG.rst
* fix: remove unnecessary diff ---------Co-authored-by: Yutaka Kondo <<yutaka.kondo@youtalk.jp>>
fix: fix ticket links in CHANGELOG.rst (#9588)
fix(cpplint): include what you use - control (#9565)
fix(autoware_smart_mpc_trajectory_follower): fix clang-diagnostic-ignored-optimization-argument (#9437)
0.39.0
update changelog

File truncated at 100 lines see the full file

Package Dependencies

Deps	Name
	ament_cmake_auto
	ament_cmake_python
	autoware_cmake
	ros2launch
	ament_cmake_ros
	ament_index_python
	ament_lint_auto
	autoware_lint_common
	autoware_testing
	ros_testing
	autoware_adapi_v1_msgs
	autoware_control_msgs
	autoware_motion_utils
	autoware_planning_msgs
	autoware_system_msgs
	autoware_utils
	autoware_vehicle_cmd_gate
	autoware_vehicle_msgs
	pybind11_vendor
	rclcpp
	rclcpp_components

System Dependencies

Name
python3-scipy
python3-torch

Dependant Packages

No known dependants.

Launch files

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Messages

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Services

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Plugins

No plugins found.

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autoware_smart_mpc_trajectory_follower package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter autoware_diagnostic_graph_test_examples tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

ROS Distro
github

API Docs
Browse Code

Package Summary

Tags	No category tags.
Version	0.47.0
License	Apache License 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description
Checkout URI	https://github.com/autowarefoundation/autoware_universe.git
VCS Type	git
VCS Version	main
Last Updated	2025-08-16
Dev Status	UNKNOWN
Released	UNRELEASED
Tags	planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Nodes to follow a trajectory by generating control commands using smart mpc

Additional Links

No additional links.

Maintainers

Masayuki Aino
Kosuke Takeuchi
Takamasa Horibe
Takayuki Murooka

Authors

Masayuki Aino

control/autoware_smart_mpc_trajectory_follower/README.md

Smart MPC Trajectory Follower

Smart MPC (Model Predictive Control) is a control algorithm that combines model predictive control and machine learning. While inheriting the advantages of model predictive control, it solves its disadvantage of modeling difficulty with a data-driven method using machine learning.

This technology makes it relatively easy to operate model predictive control, which is expensive to implement, as long as an environment for collecting data can be prepared.

</a> ## Provided features This package provides smart MPC logic for path-following control as well as mechanisms for learning and evaluation. These features are described below. ### Trajectory following control based on iLQR/MPPI The control mode can be selected from "ilqr", "mppi", or "mppi_ilqr", and can be set as `mpc_parameter:system:mode` in [mpc_param.yaml](./autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml). In "mppi_ilqr" mode, the initial value of iLQR is given by the MPPI solution. > [!NOTE] > With the default settings, the performance of "mppi" mode is limited due to an insufficient number of samples. This issue is being addressed with ongoing work to introduce GPU support. To perform a simulation, run the following command: ```bash ros2 launch autoware_launch planning_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit trajectory_follower_mode:=smart_mpc_trajectory_follower ``` > [!NOTE] > When running with the nominal model set in [nominal_param.yaml](./autoware_smart_mpc_trajectory_follower/param/nominal_param.yaml), set `trained_model_parameter:control_application:use_trained_model` to `false` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml). To run using the trained model, set `trained_model_parameter:control_application:use_trained_model` to `true`, but the trained model must have been generated according to the following procedure. ### Training of model and reflection in control To obtain training data, start autoware, perform a drive, and record rosbag data with the following commands. ```bash ros2 bag record /localization/kinematic_state /localization/acceleration /vehicle/status/steering_status /control/command/control_cmd /control/trajectory_follower/control_cmd /control/trajectory_follower/lane_departure_checker_node/debug/deviation/lateral /control/trajectory_follower/lane_departure_checker_node/debug/deviation/yaw /system/operation_mode/state /vehicle/status/control_mode /sensing/imu/imu_data /debug_mpc_x_des /debug_mpc_y_des /debug_mpc_v_des /debug_mpc_yaw_des /debug_mpc_acc_des /debug_mpc_steer_des /debug_mpc_X_des_converted /debug_mpc_x_current /debug_mpc_error_prediction /debug_mpc_max_trajectory_err /debug_mpc_emergency_stop_mode /debug_mpc_goal_stop_mode /debug_mpc_total_ctrl_time /debug_mpc_calc_u_opt_time ``` Move [rosbag2.bash](./autoware_smart_mpc_trajectory_follower/training_and_data_check/rosbag2.bash) to the rosbag directory recorded above and execute the following command on the directory ```bash bash rosbag2.bash ``` This converts rosbag data into CSV format for training models. > [!NOTE] > Note that a large number of terminals are automatically opened at runtime, but they are automatically closed after rosbag data conversion is completed. > From the time you begin this process until all terminals are closed, autoware should not be running. Instead, the same result can be obtained by executing the following command in a python environment: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.transform_rosbag_to_csv(rosbag_dir) ``` Here, `rosbag_dir` represents the rosbag directory. At this time, all CSV files in `rosbag_dir` are automatically deleted first. We move on to an explanation of how the model is trained. If `trained_model_parameter:memory_for_training:use_memory_for_training` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml) is set to `true`, training is performed on models that include LSTM, and if it is set to `false`, training is performed on models that do not include LSTM. When using LSTM, cell states and hidden states are updated based on historical time series data and reflected in the prediction. The paths of the rosbag directories used for training and validation, `dir_0`, `dir_1`, `dir_2`,..., `dir_val_0`, `dir_val_1`, `dir_val_2`,... and the directory `save_dir` where you save the models, the model can be saved in the python environment as follows: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.add_data_from_csv(dir_0, add_mode="as_train") model_trainer.add_data_from_csv(dir_1, add_mode="as_train") model_trainer.add_data_from_csv(dir_2, add_mode="as_train") ... model_trainer.add_data_from_csv(dir_val_0, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_1, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_2, add_mode="as_val") ... model_trainer.get_trained_model() model_trainer.save_models(save_dir) ``` If `add_mode` is not specified or validation data is not added, the training data is split to be used for training and validation. After performing the polynomial regression, the NN can be trained on the residuals as follows: ```python model_trainer.get_trained_model(use_polynomial_reg=True) ``` File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/control/autoware_smart_mpc_trajectory_follower/README.md)

CHANGELOG

Changelog for package autoware_smart_mpc_trajectory_follower

0.47.0 (2025-08-11)

feat: change planning output topic name to /planning/trajectory (#11135)
- change planning output topic name to /planning/trajectory
* style(pre-commit): autofix ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Contributors: Yukihiro Saito

0.46.0 (2025-06-20)

0.45.0 (2025-05-22)

Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
fix(smart_mpc): imperative mood (#10658)
Contributors: TaikiYamada4, Yuxuan Liu

0.44.2 (2025-06-10)

0.44.1 (2025-05-01)

0.44.0 (2025-04-18)

0.43.0 (2025-03-21)

Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
Contributors: Hayato Mizushima, Yutaka Kondo

0.42.0 (2025-03-03)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
Contributors: Fumiya Watanabe, 心刚

0.41.2 (2025-02-19)

chore: bump version to 0.41.1 (#10088)
Contributors: Ryohsuke Mitsudome

0.41.1 (2025-02-10)

0.41.0 (2025-01-29)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat: tier4_debug_msgs changed to autoware_internal_debug_msgs in fil… (#9851)
Contributors: Fumiya Watanabe, Vishal Chauhan

0.40.0 (2024-12-12)

Merge branch 'main' into release-0.40.0
Revert "chore(package.xml): bump version to 0.39.0 (#9587)" This reverts commit c9f0f2688c57b0f657f5c1f28f036a970682e7f5.
fix: fix ticket links in CHANGELOG.rst (#9588)
chore(package.xml): bump version to 0.39.0 (#9587)
- chore(package.xml): bump version to 0.39.0
- fix: fix ticket links in CHANGELOG.rst
* fix: remove unnecessary diff ---------Co-authored-by: Yutaka Kondo <<yutaka.kondo@youtalk.jp>>
fix: fix ticket links in CHANGELOG.rst (#9588)
fix(cpplint): include what you use - control (#9565)
fix(autoware_smart_mpc_trajectory_follower): fix clang-diagnostic-ignored-optimization-argument (#9437)
0.39.0
update changelog

File truncated at 100 lines see the full file

Package Dependencies

Deps	Name
	ament_cmake_auto
	ament_cmake_python
	autoware_cmake
	ros2launch
	ament_cmake_ros
	ament_index_python
	ament_lint_auto
	autoware_lint_common
	autoware_testing
	ros_testing
	autoware_adapi_v1_msgs
	autoware_control_msgs
	autoware_motion_utils
	autoware_planning_msgs
	autoware_system_msgs
	autoware_utils
	autoware_vehicle_cmd_gate
	autoware_vehicle_msgs
	pybind11_vendor
	rclcpp
	rclcpp_components

System Dependencies

Name
python3-scipy
python3-torch

Dependant Packages

No known dependants.

Launch files

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Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

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autoware_smart_mpc_trajectory_follower package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter autoware_diagnostic_graph_test_examples tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

ROS Distro
github

API Docs
Browse Code

Package Summary

Tags	No category tags.
Version	0.47.0
License	Apache License 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description
Checkout URI	https://github.com/autowarefoundation/autoware_universe.git
VCS Type	git
VCS Version	main
Last Updated	2025-08-16
Dev Status	UNKNOWN
Released	UNRELEASED
Tags	planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Nodes to follow a trajectory by generating control commands using smart mpc

Additional Links

No additional links.

Maintainers

Masayuki Aino
Kosuke Takeuchi
Takamasa Horibe
Takayuki Murooka

Authors

Masayuki Aino

control/autoware_smart_mpc_trajectory_follower/README.md

Smart MPC Trajectory Follower

Smart MPC (Model Predictive Control) is a control algorithm that combines model predictive control and machine learning. While inheriting the advantages of model predictive control, it solves its disadvantage of modeling difficulty with a data-driven method using machine learning.

This technology makes it relatively easy to operate model predictive control, which is expensive to implement, as long as an environment for collecting data can be prepared.

</a> ## Provided features This package provides smart MPC logic for path-following control as well as mechanisms for learning and evaluation. These features are described below. ### Trajectory following control based on iLQR/MPPI The control mode can be selected from "ilqr", "mppi", or "mppi_ilqr", and can be set as `mpc_parameter:system:mode` in [mpc_param.yaml](./autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml). In "mppi_ilqr" mode, the initial value of iLQR is given by the MPPI solution. > [!NOTE] > With the default settings, the performance of "mppi" mode is limited due to an insufficient number of samples. This issue is being addressed with ongoing work to introduce GPU support. To perform a simulation, run the following command: ```bash ros2 launch autoware_launch planning_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit trajectory_follower_mode:=smart_mpc_trajectory_follower ``` > [!NOTE] > When running with the nominal model set in [nominal_param.yaml](./autoware_smart_mpc_trajectory_follower/param/nominal_param.yaml), set `trained_model_parameter:control_application:use_trained_model` to `false` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml). To run using the trained model, set `trained_model_parameter:control_application:use_trained_model` to `true`, but the trained model must have been generated according to the following procedure. ### Training of model and reflection in control To obtain training data, start autoware, perform a drive, and record rosbag data with the following commands. ```bash ros2 bag record /localization/kinematic_state /localization/acceleration /vehicle/status/steering_status /control/command/control_cmd /control/trajectory_follower/control_cmd /control/trajectory_follower/lane_departure_checker_node/debug/deviation/lateral /control/trajectory_follower/lane_departure_checker_node/debug/deviation/yaw /system/operation_mode/state /vehicle/status/control_mode /sensing/imu/imu_data /debug_mpc_x_des /debug_mpc_y_des /debug_mpc_v_des /debug_mpc_yaw_des /debug_mpc_acc_des /debug_mpc_steer_des /debug_mpc_X_des_converted /debug_mpc_x_current /debug_mpc_error_prediction /debug_mpc_max_trajectory_err /debug_mpc_emergency_stop_mode /debug_mpc_goal_stop_mode /debug_mpc_total_ctrl_time /debug_mpc_calc_u_opt_time ``` Move [rosbag2.bash](./autoware_smart_mpc_trajectory_follower/training_and_data_check/rosbag2.bash) to the rosbag directory recorded above and execute the following command on the directory ```bash bash rosbag2.bash ``` This converts rosbag data into CSV format for training models. > [!NOTE] > Note that a large number of terminals are automatically opened at runtime, but they are automatically closed after rosbag data conversion is completed. > From the time you begin this process until all terminals are closed, autoware should not be running. Instead, the same result can be obtained by executing the following command in a python environment: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.transform_rosbag_to_csv(rosbag_dir) ``` Here, `rosbag_dir` represents the rosbag directory. At this time, all CSV files in `rosbag_dir` are automatically deleted first. We move on to an explanation of how the model is trained. If `trained_model_parameter:memory_for_training:use_memory_for_training` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml) is set to `true`, training is performed on models that include LSTM, and if it is set to `false`, training is performed on models that do not include LSTM. When using LSTM, cell states and hidden states are updated based on historical time series data and reflected in the prediction. The paths of the rosbag directories used for training and validation, `dir_0`, `dir_1`, `dir_2`,..., `dir_val_0`, `dir_val_1`, `dir_val_2`,... and the directory `save_dir` where you save the models, the model can be saved in the python environment as follows: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.add_data_from_csv(dir_0, add_mode="as_train") model_trainer.add_data_from_csv(dir_1, add_mode="as_train") model_trainer.add_data_from_csv(dir_2, add_mode="as_train") ... model_trainer.add_data_from_csv(dir_val_0, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_1, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_2, add_mode="as_val") ... model_trainer.get_trained_model() model_trainer.save_models(save_dir) ``` If `add_mode` is not specified or validation data is not added, the training data is split to be used for training and validation. After performing the polynomial regression, the NN can be trained on the residuals as follows: ```python model_trainer.get_trained_model(use_polynomial_reg=True) ``` File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/control/autoware_smart_mpc_trajectory_follower/README.md)

CHANGELOG

Changelog for package autoware_smart_mpc_trajectory_follower

0.47.0 (2025-08-11)

feat: change planning output topic name to /planning/trajectory (#11135)
- change planning output topic name to /planning/trajectory
* style(pre-commit): autofix ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Contributors: Yukihiro Saito

0.46.0 (2025-06-20)

0.45.0 (2025-05-22)

Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
fix(smart_mpc): imperative mood (#10658)
Contributors: TaikiYamada4, Yuxuan Liu

0.44.2 (2025-06-10)

0.44.1 (2025-05-01)

0.44.0 (2025-04-18)

0.43.0 (2025-03-21)

Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
Contributors: Hayato Mizushima, Yutaka Kondo

0.42.0 (2025-03-03)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
Contributors: Fumiya Watanabe, 心刚

0.41.2 (2025-02-19)

chore: bump version to 0.41.1 (#10088)
Contributors: Ryohsuke Mitsudome

0.41.1 (2025-02-10)

0.41.0 (2025-01-29)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat: tier4_debug_msgs changed to autoware_internal_debug_msgs in fil… (#9851)
Contributors: Fumiya Watanabe, Vishal Chauhan

0.40.0 (2024-12-12)

Merge branch 'main' into release-0.40.0
Revert "chore(package.xml): bump version to 0.39.0 (#9587)" This reverts commit c9f0f2688c57b0f657f5c1f28f036a970682e7f5.
fix: fix ticket links in CHANGELOG.rst (#9588)
chore(package.xml): bump version to 0.39.0 (#9587)
- chore(package.xml): bump version to 0.39.0
- fix: fix ticket links in CHANGELOG.rst
* fix: remove unnecessary diff ---------Co-authored-by: Yutaka Kondo <<yutaka.kondo@youtalk.jp>>
fix: fix ticket links in CHANGELOG.rst (#9588)
fix(cpplint): include what you use - control (#9565)
fix(autoware_smart_mpc_trajectory_follower): fix clang-diagnostic-ignored-optimization-argument (#9437)
0.39.0
update changelog

File truncated at 100 lines see the full file

Package Dependencies

Deps	Name
	ament_cmake_auto
	ament_cmake_python
	autoware_cmake
	ros2launch
	ament_cmake_ros
	ament_index_python
	ament_lint_auto
	autoware_lint_common
	autoware_testing
	ros_testing
	autoware_adapi_v1_msgs
	autoware_control_msgs
	autoware_motion_utils
	autoware_planning_msgs
	autoware_system_msgs
	autoware_utils
	autoware_vehicle_cmd_gate
	autoware_vehicle_msgs
	pybind11_vendor
	rclcpp
	rclcpp_components

System Dependencies

Name
python3-scipy
python3-torch

Dependant Packages

No known dependants.

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Messages

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Services

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Plugins

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autoware_smart_mpc_trajectory_follower package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter autoware_diagnostic_graph_test_examples tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

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github

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Package Summary

Tags	No category tags.
Version	0.47.0
License	Apache License 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description
Checkout URI	https://github.com/autowarefoundation/autoware_universe.git
VCS Type	git
VCS Version	main
Last Updated	2025-08-16
Dev Status	UNKNOWN
Released	UNRELEASED
Tags	planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Nodes to follow a trajectory by generating control commands using smart mpc

Additional Links

No additional links.

Maintainers

Masayuki Aino
Kosuke Takeuchi
Takamasa Horibe
Takayuki Murooka

Authors

Masayuki Aino

control/autoware_smart_mpc_trajectory_follower/README.md

Smart MPC Trajectory Follower

Smart MPC (Model Predictive Control) is a control algorithm that combines model predictive control and machine learning. While inheriting the advantages of model predictive control, it solves its disadvantage of modeling difficulty with a data-driven method using machine learning.

This technology makes it relatively easy to operate model predictive control, which is expensive to implement, as long as an environment for collecting data can be prepared.

</a> ## Provided features This package provides smart MPC logic for path-following control as well as mechanisms for learning and evaluation. These features are described below. ### Trajectory following control based on iLQR/MPPI The control mode can be selected from "ilqr", "mppi", or "mppi_ilqr", and can be set as `mpc_parameter:system:mode` in [mpc_param.yaml](./autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml). In "mppi_ilqr" mode, the initial value of iLQR is given by the MPPI solution. > [!NOTE] > With the default settings, the performance of "mppi" mode is limited due to an insufficient number of samples. This issue is being addressed with ongoing work to introduce GPU support. To perform a simulation, run the following command: ```bash ros2 launch autoware_launch planning_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit trajectory_follower_mode:=smart_mpc_trajectory_follower ``` > [!NOTE] > When running with the nominal model set in [nominal_param.yaml](./autoware_smart_mpc_trajectory_follower/param/nominal_param.yaml), set `trained_model_parameter:control_application:use_trained_model` to `false` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml). To run using the trained model, set `trained_model_parameter:control_application:use_trained_model` to `true`, but the trained model must have been generated according to the following procedure. ### Training of model and reflection in control To obtain training data, start autoware, perform a drive, and record rosbag data with the following commands. ```bash ros2 bag record /localization/kinematic_state /localization/acceleration /vehicle/status/steering_status /control/command/control_cmd /control/trajectory_follower/control_cmd /control/trajectory_follower/lane_departure_checker_node/debug/deviation/lateral /control/trajectory_follower/lane_departure_checker_node/debug/deviation/yaw /system/operation_mode/state /vehicle/status/control_mode /sensing/imu/imu_data /debug_mpc_x_des /debug_mpc_y_des /debug_mpc_v_des /debug_mpc_yaw_des /debug_mpc_acc_des /debug_mpc_steer_des /debug_mpc_X_des_converted /debug_mpc_x_current /debug_mpc_error_prediction /debug_mpc_max_trajectory_err /debug_mpc_emergency_stop_mode /debug_mpc_goal_stop_mode /debug_mpc_total_ctrl_time /debug_mpc_calc_u_opt_time ``` Move [rosbag2.bash](./autoware_smart_mpc_trajectory_follower/training_and_data_check/rosbag2.bash) to the rosbag directory recorded above and execute the following command on the directory ```bash bash rosbag2.bash ``` This converts rosbag data into CSV format for training models. > [!NOTE] > Note that a large number of terminals are automatically opened at runtime, but they are automatically closed after rosbag data conversion is completed. > From the time you begin this process until all terminals are closed, autoware should not be running. Instead, the same result can be obtained by executing the following command in a python environment: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.transform_rosbag_to_csv(rosbag_dir) ``` Here, `rosbag_dir` represents the rosbag directory. At this time, all CSV files in `rosbag_dir` are automatically deleted first. We move on to an explanation of how the model is trained. If `trained_model_parameter:memory_for_training:use_memory_for_training` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml) is set to `true`, training is performed on models that include LSTM, and if it is set to `false`, training is performed on models that do not include LSTM. When using LSTM, cell states and hidden states are updated based on historical time series data and reflected in the prediction. The paths of the rosbag directories used for training and validation, `dir_0`, `dir_1`, `dir_2`,..., `dir_val_0`, `dir_val_1`, `dir_val_2`,... and the directory `save_dir` where you save the models, the model can be saved in the python environment as follows: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.add_data_from_csv(dir_0, add_mode="as_train") model_trainer.add_data_from_csv(dir_1, add_mode="as_train") model_trainer.add_data_from_csv(dir_2, add_mode="as_train") ... model_trainer.add_data_from_csv(dir_val_0, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_1, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_2, add_mode="as_val") ... model_trainer.get_trained_model() model_trainer.save_models(save_dir) ``` If `add_mode` is not specified or validation data is not added, the training data is split to be used for training and validation. After performing the polynomial regression, the NN can be trained on the residuals as follows: ```python model_trainer.get_trained_model(use_polynomial_reg=True) ``` File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/control/autoware_smart_mpc_trajectory_follower/README.md)

CHANGELOG

Changelog for package autoware_smart_mpc_trajectory_follower

0.47.0 (2025-08-11)

feat: change planning output topic name to /planning/trajectory (#11135)
- change planning output topic name to /planning/trajectory
* style(pre-commit): autofix ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Contributors: Yukihiro Saito

0.46.0 (2025-06-20)

0.45.0 (2025-05-22)

Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
fix(smart_mpc): imperative mood (#10658)
Contributors: TaikiYamada4, Yuxuan Liu

0.44.2 (2025-06-10)

0.44.1 (2025-05-01)

0.44.0 (2025-04-18)

0.43.0 (2025-03-21)

Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
Contributors: Hayato Mizushima, Yutaka Kondo

0.42.0 (2025-03-03)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
Contributors: Fumiya Watanabe, 心刚

0.41.2 (2025-02-19)

chore: bump version to 0.41.1 (#10088)
Contributors: Ryohsuke Mitsudome

0.41.1 (2025-02-10)

0.41.0 (2025-01-29)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat: tier4_debug_msgs changed to autoware_internal_debug_msgs in fil… (#9851)
Contributors: Fumiya Watanabe, Vishal Chauhan

0.40.0 (2024-12-12)

Merge branch 'main' into release-0.40.0
Revert "chore(package.xml): bump version to 0.39.0 (#9587)" This reverts commit c9f0f2688c57b0f657f5c1f28f036a970682e7f5.
fix: fix ticket links in CHANGELOG.rst (#9588)
chore(package.xml): bump version to 0.39.0 (#9587)
- chore(package.xml): bump version to 0.39.0
- fix: fix ticket links in CHANGELOG.rst
* fix: remove unnecessary diff ---------Co-authored-by: Yutaka Kondo <<yutaka.kondo@youtalk.jp>>
fix: fix ticket links in CHANGELOG.rst (#9588)
fix(cpplint): include what you use - control (#9565)
fix(autoware_smart_mpc_trajectory_follower): fix clang-diagnostic-ignored-optimization-argument (#9437)
0.39.0
update changelog

File truncated at 100 lines see the full file

Package Dependencies

Deps	Name
	ament_cmake_auto
	ament_cmake_python
	autoware_cmake
	ros2launch
	ament_cmake_ros
	ament_index_python
	ament_lint_auto
	autoware_lint_common
	autoware_testing
	ros_testing
	autoware_adapi_v1_msgs
	autoware_control_msgs
	autoware_motion_utils
	autoware_planning_msgs
	autoware_system_msgs
	autoware_utils
	autoware_vehicle_cmd_gate
	autoware_vehicle_msgs
	pybind11_vendor
	rclcpp
	rclcpp_components

System Dependencies

Name
python3-scipy
python3-torch

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

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autoware_smart_mpc_trajectory_follower package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter autoware_diagnostic_graph_test_examples tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

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github

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Package Summary

Tags	No category tags.
Version	0.47.0
License	Apache License 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description
Checkout URI	https://github.com/autowarefoundation/autoware_universe.git
VCS Type	git
VCS Version	main
Last Updated	2025-08-16
Dev Status	UNKNOWN
Released	UNRELEASED
Tags	planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Nodes to follow a trajectory by generating control commands using smart mpc

Additional Links

No additional links.

Maintainers

Masayuki Aino
Kosuke Takeuchi
Takamasa Horibe
Takayuki Murooka

Authors

Masayuki Aino

control/autoware_smart_mpc_trajectory_follower/README.md

Smart MPC Trajectory Follower

Smart MPC (Model Predictive Control) is a control algorithm that combines model predictive control and machine learning. While inheriting the advantages of model predictive control, it solves its disadvantage of modeling difficulty with a data-driven method using machine learning.

This technology makes it relatively easy to operate model predictive control, which is expensive to implement, as long as an environment for collecting data can be prepared.

</a> ## Provided features This package provides smart MPC logic for path-following control as well as mechanisms for learning and evaluation. These features are described below. ### Trajectory following control based on iLQR/MPPI The control mode can be selected from "ilqr", "mppi", or "mppi_ilqr", and can be set as `mpc_parameter:system:mode` in [mpc_param.yaml](./autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml). In "mppi_ilqr" mode, the initial value of iLQR is given by the MPPI solution. > [!NOTE] > With the default settings, the performance of "mppi" mode is limited due to an insufficient number of samples. This issue is being addressed with ongoing work to introduce GPU support. To perform a simulation, run the following command: ```bash ros2 launch autoware_launch planning_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit trajectory_follower_mode:=smart_mpc_trajectory_follower ``` > [!NOTE] > When running with the nominal model set in [nominal_param.yaml](./autoware_smart_mpc_trajectory_follower/param/nominal_param.yaml), set `trained_model_parameter:control_application:use_trained_model` to `false` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml). To run using the trained model, set `trained_model_parameter:control_application:use_trained_model` to `true`, but the trained model must have been generated according to the following procedure. ### Training of model and reflection in control To obtain training data, start autoware, perform a drive, and record rosbag data with the following commands. ```bash ros2 bag record /localization/kinematic_state /localization/acceleration /vehicle/status/steering_status /control/command/control_cmd /control/trajectory_follower/control_cmd /control/trajectory_follower/lane_departure_checker_node/debug/deviation/lateral /control/trajectory_follower/lane_departure_checker_node/debug/deviation/yaw /system/operation_mode/state /vehicle/status/control_mode /sensing/imu/imu_data /debug_mpc_x_des /debug_mpc_y_des /debug_mpc_v_des /debug_mpc_yaw_des /debug_mpc_acc_des /debug_mpc_steer_des /debug_mpc_X_des_converted /debug_mpc_x_current /debug_mpc_error_prediction /debug_mpc_max_trajectory_err /debug_mpc_emergency_stop_mode /debug_mpc_goal_stop_mode /debug_mpc_total_ctrl_time /debug_mpc_calc_u_opt_time ``` Move [rosbag2.bash](./autoware_smart_mpc_trajectory_follower/training_and_data_check/rosbag2.bash) to the rosbag directory recorded above and execute the following command on the directory ```bash bash rosbag2.bash ``` This converts rosbag data into CSV format for training models. > [!NOTE] > Note that a large number of terminals are automatically opened at runtime, but they are automatically closed after rosbag data conversion is completed. > From the time you begin this process until all terminals are closed, autoware should not be running. Instead, the same result can be obtained by executing the following command in a python environment: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.transform_rosbag_to_csv(rosbag_dir) ``` Here, `rosbag_dir` represents the rosbag directory. At this time, all CSV files in `rosbag_dir` are automatically deleted first. We move on to an explanation of how the model is trained. If `trained_model_parameter:memory_for_training:use_memory_for_training` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml) is set to `true`, training is performed on models that include LSTM, and if it is set to `false`, training is performed on models that do not include LSTM. When using LSTM, cell states and hidden states are updated based on historical time series data and reflected in the prediction. The paths of the rosbag directories used for training and validation, `dir_0`, `dir_1`, `dir_2`,..., `dir_val_0`, `dir_val_1`, `dir_val_2`,... and the directory `save_dir` where you save the models, the model can be saved in the python environment as follows: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.add_data_from_csv(dir_0, add_mode="as_train") model_trainer.add_data_from_csv(dir_1, add_mode="as_train") model_trainer.add_data_from_csv(dir_2, add_mode="as_train") ... model_trainer.add_data_from_csv(dir_val_0, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_1, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_2, add_mode="as_val") ... model_trainer.get_trained_model() model_trainer.save_models(save_dir) ``` If `add_mode` is not specified or validation data is not added, the training data is split to be used for training and validation. After performing the polynomial regression, the NN can be trained on the residuals as follows: ```python model_trainer.get_trained_model(use_polynomial_reg=True) ``` File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/control/autoware_smart_mpc_trajectory_follower/README.md)

CHANGELOG

Changelog for package autoware_smart_mpc_trajectory_follower

0.47.0 (2025-08-11)

feat: change planning output topic name to /planning/trajectory (#11135)
- change planning output topic name to /planning/trajectory
* style(pre-commit): autofix ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Contributors: Yukihiro Saito

0.46.0 (2025-06-20)

0.45.0 (2025-05-22)

Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
fix(smart_mpc): imperative mood (#10658)
Contributors: TaikiYamada4, Yuxuan Liu

0.44.2 (2025-06-10)

0.44.1 (2025-05-01)

0.44.0 (2025-04-18)

0.43.0 (2025-03-21)

Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
Contributors: Hayato Mizushima, Yutaka Kondo

0.42.0 (2025-03-03)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
Contributors: Fumiya Watanabe, 心刚

0.41.2 (2025-02-19)

chore: bump version to 0.41.1 (#10088)
Contributors: Ryohsuke Mitsudome

0.41.1 (2025-02-10)

0.41.0 (2025-01-29)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat: tier4_debug_msgs changed to autoware_internal_debug_msgs in fil… (#9851)
Contributors: Fumiya Watanabe, Vishal Chauhan

0.40.0 (2024-12-12)

Merge branch 'main' into release-0.40.0
Revert "chore(package.xml): bump version to 0.39.0 (#9587)" This reverts commit c9f0f2688c57b0f657f5c1f28f036a970682e7f5.
fix: fix ticket links in CHANGELOG.rst (#9588)
chore(package.xml): bump version to 0.39.0 (#9587)
- chore(package.xml): bump version to 0.39.0
- fix: fix ticket links in CHANGELOG.rst
* fix: remove unnecessary diff ---------Co-authored-by: Yutaka Kondo <<yutaka.kondo@youtalk.jp>>
fix: fix ticket links in CHANGELOG.rst (#9588)
fix(cpplint): include what you use - control (#9565)
fix(autoware_smart_mpc_trajectory_follower): fix clang-diagnostic-ignored-optimization-argument (#9437)
0.39.0
update changelog

File truncated at 100 lines see the full file

Package Dependencies

Deps	Name
	ament_cmake_auto
	ament_cmake_python
	autoware_cmake
	ros2launch
	ament_cmake_ros
	ament_index_python
	ament_lint_auto
	autoware_lint_common
	autoware_testing
	ros_testing
	autoware_adapi_v1_msgs
	autoware_control_msgs
	autoware_motion_utils
	autoware_planning_msgs
	autoware_system_msgs
	autoware_utils
	autoware_vehicle_cmd_gate
	autoware_vehicle_msgs
	pybind11_vendor
	rclcpp
	rclcpp_components

System Dependencies

Name
python3-scipy
python3-torch

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged `autoware_smart_mpc_trajectory_follower` at Robotics Stack Exchange

API Docs Browse Code

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autoware_smart_mpc_trajectory_follower package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter autoware_diagnostic_graph_test_examples tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

ROS Distro
github

API Docs
Browse Code

Package Summary

Tags	No category tags.
Version	0.47.0
License	Apache License 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description
Checkout URI	https://github.com/autowarefoundation/autoware_universe.git
VCS Type	git
VCS Version	main
Last Updated	2025-08-16
Dev Status	UNKNOWN
Released	UNRELEASED
Tags	planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Nodes to follow a trajectory by generating control commands using smart mpc

Additional Links

No additional links.

Maintainers

Masayuki Aino
Kosuke Takeuchi
Takamasa Horibe
Takayuki Murooka

Authors

Masayuki Aino

control/autoware_smart_mpc_trajectory_follower/README.md

Smart MPC Trajectory Follower

Smart MPC (Model Predictive Control) is a control algorithm that combines model predictive control and machine learning. While inheriting the advantages of model predictive control, it solves its disadvantage of modeling difficulty with a data-driven method using machine learning.

This technology makes it relatively easy to operate model predictive control, which is expensive to implement, as long as an environment for collecting data can be prepared.

</a> ## Provided features This package provides smart MPC logic for path-following control as well as mechanisms for learning and evaluation. These features are described below. ### Trajectory following control based on iLQR/MPPI The control mode can be selected from "ilqr", "mppi", or "mppi_ilqr", and can be set as `mpc_parameter:system:mode` in [mpc_param.yaml](./autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml). In "mppi_ilqr" mode, the initial value of iLQR is given by the MPPI solution. > [!NOTE] > With the default settings, the performance of "mppi" mode is limited due to an insufficient number of samples. This issue is being addressed with ongoing work to introduce GPU support. To perform a simulation, run the following command: ```bash ros2 launch autoware_launch planning_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit trajectory_follower_mode:=smart_mpc_trajectory_follower ``` > [!NOTE] > When running with the nominal model set in [nominal_param.yaml](./autoware_smart_mpc_trajectory_follower/param/nominal_param.yaml), set `trained_model_parameter:control_application:use_trained_model` to `false` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml). To run using the trained model, set `trained_model_parameter:control_application:use_trained_model` to `true`, but the trained model must have been generated according to the following procedure. ### Training of model and reflection in control To obtain training data, start autoware, perform a drive, and record rosbag data with the following commands. ```bash ros2 bag record /localization/kinematic_state /localization/acceleration /vehicle/status/steering_status /control/command/control_cmd /control/trajectory_follower/control_cmd /control/trajectory_follower/lane_departure_checker_node/debug/deviation/lateral /control/trajectory_follower/lane_departure_checker_node/debug/deviation/yaw /system/operation_mode/state /vehicle/status/control_mode /sensing/imu/imu_data /debug_mpc_x_des /debug_mpc_y_des /debug_mpc_v_des /debug_mpc_yaw_des /debug_mpc_acc_des /debug_mpc_steer_des /debug_mpc_X_des_converted /debug_mpc_x_current /debug_mpc_error_prediction /debug_mpc_max_trajectory_err /debug_mpc_emergency_stop_mode /debug_mpc_goal_stop_mode /debug_mpc_total_ctrl_time /debug_mpc_calc_u_opt_time ``` Move [rosbag2.bash](./autoware_smart_mpc_trajectory_follower/training_and_data_check/rosbag2.bash) to the rosbag directory recorded above and execute the following command on the directory ```bash bash rosbag2.bash ``` This converts rosbag data into CSV format for training models. > [!NOTE] > Note that a large number of terminals are automatically opened at runtime, but they are automatically closed after rosbag data conversion is completed. > From the time you begin this process until all terminals are closed, autoware should not be running. Instead, the same result can be obtained by executing the following command in a python environment: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.transform_rosbag_to_csv(rosbag_dir) ``` Here, `rosbag_dir` represents the rosbag directory. At this time, all CSV files in `rosbag_dir` are automatically deleted first. We move on to an explanation of how the model is trained. If `trained_model_parameter:memory_for_training:use_memory_for_training` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml) is set to `true`, training is performed on models that include LSTM, and if it is set to `false`, training is performed on models that do not include LSTM. When using LSTM, cell states and hidden states are updated based on historical time series data and reflected in the prediction. The paths of the rosbag directories used for training and validation, `dir_0`, `dir_1`, `dir_2`,..., `dir_val_0`, `dir_val_1`, `dir_val_2`,... and the directory `save_dir` where you save the models, the model can be saved in the python environment as follows: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.add_data_from_csv(dir_0, add_mode="as_train") model_trainer.add_data_from_csv(dir_1, add_mode="as_train") model_trainer.add_data_from_csv(dir_2, add_mode="as_train") ... model_trainer.add_data_from_csv(dir_val_0, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_1, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_2, add_mode="as_val") ... model_trainer.get_trained_model() model_trainer.save_models(save_dir) ``` If `add_mode` is not specified or validation data is not added, the training data is split to be used for training and validation. After performing the polynomial regression, the NN can be trained on the residuals as follows: ```python model_trainer.get_trained_model(use_polynomial_reg=True) ``` File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/control/autoware_smart_mpc_trajectory_follower/README.md)

CHANGELOG

Changelog for package autoware_smart_mpc_trajectory_follower

0.47.0 (2025-08-11)

feat: change planning output topic name to /planning/trajectory (#11135)
- change planning output topic name to /planning/trajectory
* style(pre-commit): autofix ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Contributors: Yukihiro Saito

0.46.0 (2025-06-20)

0.45.0 (2025-05-22)

Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
fix(smart_mpc): imperative mood (#10658)
Contributors: TaikiYamada4, Yuxuan Liu

0.44.2 (2025-06-10)

0.44.1 (2025-05-01)

0.44.0 (2025-04-18)

0.43.0 (2025-03-21)

Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
Contributors: Hayato Mizushima, Yutaka Kondo

0.42.0 (2025-03-03)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
Contributors: Fumiya Watanabe, 心刚

0.41.2 (2025-02-19)

chore: bump version to 0.41.1 (#10088)
Contributors: Ryohsuke Mitsudome

0.41.1 (2025-02-10)

0.41.0 (2025-01-29)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat: tier4_debug_msgs changed to autoware_internal_debug_msgs in fil… (#9851)
Contributors: Fumiya Watanabe, Vishal Chauhan

0.40.0 (2024-12-12)

Merge branch 'main' into release-0.40.0
Revert "chore(package.xml): bump version to 0.39.0 (#9587)" This reverts commit c9f0f2688c57b0f657f5c1f28f036a970682e7f5.
fix: fix ticket links in CHANGELOG.rst (#9588)
chore(package.xml): bump version to 0.39.0 (#9587)
- chore(package.xml): bump version to 0.39.0
- fix: fix ticket links in CHANGELOG.rst
* fix: remove unnecessary diff ---------Co-authored-by: Yutaka Kondo <<yutaka.kondo@youtalk.jp>>
fix: fix ticket links in CHANGELOG.rst (#9588)
fix(cpplint): include what you use - control (#9565)
fix(autoware_smart_mpc_trajectory_follower): fix clang-diagnostic-ignored-optimization-argument (#9437)
0.39.0
update changelog

File truncated at 100 lines see the full file

Package Dependencies

Deps	Name
	ament_cmake_auto
	ament_cmake_python
	autoware_cmake
	ros2launch
	ament_cmake_ros
	ament_index_python
	ament_lint_auto
	autoware_lint_common
	autoware_testing
	ros_testing
	autoware_adapi_v1_msgs
	autoware_control_msgs
	autoware_motion_utils
	autoware_planning_msgs
	autoware_system_msgs
	autoware_utils
	autoware_vehicle_cmd_gate
	autoware_vehicle_msgs
	pybind11_vendor
	rclcpp
	rclcpp_components

System Dependencies

Name
python3-scipy
python3-torch

Dependant Packages

No known dependants.

Launch files

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Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

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autoware_smart_mpc_trajectory_follower package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter autoware_diagnostic_graph_test_examples tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

ROS Distro
github

API Docs
Browse Code

Package Summary

Tags	No category tags.
Version	0.47.0
License	Apache License 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description
Checkout URI	https://github.com/autowarefoundation/autoware_universe.git
VCS Type	git
VCS Version	main
Last Updated	2025-08-16
Dev Status	UNKNOWN
Released	UNRELEASED
Tags	planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Nodes to follow a trajectory by generating control commands using smart mpc

Additional Links

No additional links.

Maintainers

Masayuki Aino
Kosuke Takeuchi
Takamasa Horibe
Takayuki Murooka

Authors

Masayuki Aino

control/autoware_smart_mpc_trajectory_follower/README.md

Smart MPC Trajectory Follower

Smart MPC (Model Predictive Control) is a control algorithm that combines model predictive control and machine learning. While inheriting the advantages of model predictive control, it solves its disadvantage of modeling difficulty with a data-driven method using machine learning.

This technology makes it relatively easy to operate model predictive control, which is expensive to implement, as long as an environment for collecting data can be prepared.

</a> ## Provided features This package provides smart MPC logic for path-following control as well as mechanisms for learning and evaluation. These features are described below. ### Trajectory following control based on iLQR/MPPI The control mode can be selected from "ilqr", "mppi", or "mppi_ilqr", and can be set as `mpc_parameter:system:mode` in [mpc_param.yaml](./autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml). In "mppi_ilqr" mode, the initial value of iLQR is given by the MPPI solution. > [!NOTE] > With the default settings, the performance of "mppi" mode is limited due to an insufficient number of samples. This issue is being addressed with ongoing work to introduce GPU support. To perform a simulation, run the following command: ```bash ros2 launch autoware_launch planning_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit trajectory_follower_mode:=smart_mpc_trajectory_follower ``` > [!NOTE] > When running with the nominal model set in [nominal_param.yaml](./autoware_smart_mpc_trajectory_follower/param/nominal_param.yaml), set `trained_model_parameter:control_application:use_trained_model` to `false` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml). To run using the trained model, set `trained_model_parameter:control_application:use_trained_model` to `true`, but the trained model must have been generated according to the following procedure. ### Training of model and reflection in control To obtain training data, start autoware, perform a drive, and record rosbag data with the following commands. ```bash ros2 bag record /localization/kinematic_state /localization/acceleration /vehicle/status/steering_status /control/command/control_cmd /control/trajectory_follower/control_cmd /control/trajectory_follower/lane_departure_checker_node/debug/deviation/lateral /control/trajectory_follower/lane_departure_checker_node/debug/deviation/yaw /system/operation_mode/state /vehicle/status/control_mode /sensing/imu/imu_data /debug_mpc_x_des /debug_mpc_y_des /debug_mpc_v_des /debug_mpc_yaw_des /debug_mpc_acc_des /debug_mpc_steer_des /debug_mpc_X_des_converted /debug_mpc_x_current /debug_mpc_error_prediction /debug_mpc_max_trajectory_err /debug_mpc_emergency_stop_mode /debug_mpc_goal_stop_mode /debug_mpc_total_ctrl_time /debug_mpc_calc_u_opt_time ``` Move [rosbag2.bash](./autoware_smart_mpc_trajectory_follower/training_and_data_check/rosbag2.bash) to the rosbag directory recorded above and execute the following command on the directory ```bash bash rosbag2.bash ``` This converts rosbag data into CSV format for training models. > [!NOTE] > Note that a large number of terminals are automatically opened at runtime, but they are automatically closed after rosbag data conversion is completed. > From the time you begin this process until all terminals are closed, autoware should not be running. Instead, the same result can be obtained by executing the following command in a python environment: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.transform_rosbag_to_csv(rosbag_dir) ``` Here, `rosbag_dir` represents the rosbag directory. At this time, all CSV files in `rosbag_dir` are automatically deleted first. We move on to an explanation of how the model is trained. If `trained_model_parameter:memory_for_training:use_memory_for_training` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml) is set to `true`, training is performed on models that include LSTM, and if it is set to `false`, training is performed on models that do not include LSTM. When using LSTM, cell states and hidden states are updated based on historical time series data and reflected in the prediction. The paths of the rosbag directories used for training and validation, `dir_0`, `dir_1`, `dir_2`,..., `dir_val_0`, `dir_val_1`, `dir_val_2`,... and the directory `save_dir` where you save the models, the model can be saved in the python environment as follows: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.add_data_from_csv(dir_0, add_mode="as_train") model_trainer.add_data_from_csv(dir_1, add_mode="as_train") model_trainer.add_data_from_csv(dir_2, add_mode="as_train") ... model_trainer.add_data_from_csv(dir_val_0, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_1, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_2, add_mode="as_val") ... model_trainer.get_trained_model() model_trainer.save_models(save_dir) ``` If `add_mode` is not specified or validation data is not added, the training data is split to be used for training and validation. After performing the polynomial regression, the NN can be trained on the residuals as follows: ```python model_trainer.get_trained_model(use_polynomial_reg=True) ``` File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/control/autoware_smart_mpc_trajectory_follower/README.md)

CHANGELOG

Changelog for package autoware_smart_mpc_trajectory_follower

0.47.0 (2025-08-11)

feat: change planning output topic name to /planning/trajectory (#11135)
- change planning output topic name to /planning/trajectory
* style(pre-commit): autofix ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Contributors: Yukihiro Saito

0.46.0 (2025-06-20)

0.45.0 (2025-05-22)

Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
fix(smart_mpc): imperative mood (#10658)
Contributors: TaikiYamada4, Yuxuan Liu

0.44.2 (2025-06-10)

0.44.1 (2025-05-01)

0.44.0 (2025-04-18)

0.43.0 (2025-03-21)

Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
Contributors: Hayato Mizushima, Yutaka Kondo

0.42.0 (2025-03-03)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
Contributors: Fumiya Watanabe, 心刚

0.41.2 (2025-02-19)

chore: bump version to 0.41.1 (#10088)
Contributors: Ryohsuke Mitsudome

0.41.1 (2025-02-10)

0.41.0 (2025-01-29)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat: tier4_debug_msgs changed to autoware_internal_debug_msgs in fil… (#9851)
Contributors: Fumiya Watanabe, Vishal Chauhan

0.40.0 (2024-12-12)

Merge branch 'main' into release-0.40.0
Revert "chore(package.xml): bump version to 0.39.0 (#9587)" This reverts commit c9f0f2688c57b0f657f5c1f28f036a970682e7f5.
fix: fix ticket links in CHANGELOG.rst (#9588)
chore(package.xml): bump version to 0.39.0 (#9587)
- chore(package.xml): bump version to 0.39.0
- fix: fix ticket links in CHANGELOG.rst
* fix: remove unnecessary diff ---------Co-authored-by: Yutaka Kondo <<yutaka.kondo@youtalk.jp>>
fix: fix ticket links in CHANGELOG.rst (#9588)
fix(cpplint): include what you use - control (#9565)
fix(autoware_smart_mpc_trajectory_follower): fix clang-diagnostic-ignored-optimization-argument (#9437)
0.39.0
update changelog

File truncated at 100 lines see the full file

Package Dependencies

Deps	Name
	ament_cmake_auto
	ament_cmake_python
	autoware_cmake
	ros2launch
	ament_cmake_ros
	ament_index_python
	ament_lint_auto
	autoware_lint_common
	autoware_testing
	ros_testing
	autoware_adapi_v1_msgs
	autoware_control_msgs
	autoware_motion_utils
	autoware_planning_msgs
	autoware_system_msgs
	autoware_utils
	autoware_vehicle_cmd_gate
	autoware_vehicle_msgs
	pybind11_vendor
	rclcpp
	rclcpp_components

System Dependencies

Name
python3-scipy
python3-torch

Dependant Packages

No known dependants.

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Messages

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Services

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Plugins

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autoware_smart_mpc_trajectory_follower package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter autoware_diagnostic_graph_test_examples tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

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Package Summary

Tags	No category tags.
Version	0.47.0
License	Apache License 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description
Checkout URI	https://github.com/autowarefoundation/autoware_universe.git
VCS Type	git
VCS Version	main
Last Updated	2025-08-16
Dev Status	UNKNOWN
Released	UNRELEASED
Tags	planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Nodes to follow a trajectory by generating control commands using smart mpc

Additional Links

No additional links.

Maintainers

Masayuki Aino
Kosuke Takeuchi
Takamasa Horibe
Takayuki Murooka

Authors

Masayuki Aino

control/autoware_smart_mpc_trajectory_follower/README.md

Smart MPC Trajectory Follower

Smart MPC (Model Predictive Control) is a control algorithm that combines model predictive control and machine learning. While inheriting the advantages of model predictive control, it solves its disadvantage of modeling difficulty with a data-driven method using machine learning.

This technology makes it relatively easy to operate model predictive control, which is expensive to implement, as long as an environment for collecting data can be prepared.

</a> ## Provided features This package provides smart MPC logic for path-following control as well as mechanisms for learning and evaluation. These features are described below. ### Trajectory following control based on iLQR/MPPI The control mode can be selected from "ilqr", "mppi", or "mppi_ilqr", and can be set as `mpc_parameter:system:mode` in [mpc_param.yaml](./autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml). In "mppi_ilqr" mode, the initial value of iLQR is given by the MPPI solution. > [!NOTE] > With the default settings, the performance of "mppi" mode is limited due to an insufficient number of samples. This issue is being addressed with ongoing work to introduce GPU support. To perform a simulation, run the following command: ```bash ros2 launch autoware_launch planning_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit trajectory_follower_mode:=smart_mpc_trajectory_follower ``` > [!NOTE] > When running with the nominal model set in [nominal_param.yaml](./autoware_smart_mpc_trajectory_follower/param/nominal_param.yaml), set `trained_model_parameter:control_application:use_trained_model` to `false` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml). To run using the trained model, set `trained_model_parameter:control_application:use_trained_model` to `true`, but the trained model must have been generated according to the following procedure. ### Training of model and reflection in control To obtain training data, start autoware, perform a drive, and record rosbag data with the following commands. ```bash ros2 bag record /localization/kinematic_state /localization/acceleration /vehicle/status/steering_status /control/command/control_cmd /control/trajectory_follower/control_cmd /control/trajectory_follower/lane_departure_checker_node/debug/deviation/lateral /control/trajectory_follower/lane_departure_checker_node/debug/deviation/yaw /system/operation_mode/state /vehicle/status/control_mode /sensing/imu/imu_data /debug_mpc_x_des /debug_mpc_y_des /debug_mpc_v_des /debug_mpc_yaw_des /debug_mpc_acc_des /debug_mpc_steer_des /debug_mpc_X_des_converted /debug_mpc_x_current /debug_mpc_error_prediction /debug_mpc_max_trajectory_err /debug_mpc_emergency_stop_mode /debug_mpc_goal_stop_mode /debug_mpc_total_ctrl_time /debug_mpc_calc_u_opt_time ``` Move [rosbag2.bash](./autoware_smart_mpc_trajectory_follower/training_and_data_check/rosbag2.bash) to the rosbag directory recorded above and execute the following command on the directory ```bash bash rosbag2.bash ``` This converts rosbag data into CSV format for training models. > [!NOTE] > Note that a large number of terminals are automatically opened at runtime, but they are automatically closed after rosbag data conversion is completed. > From the time you begin this process until all terminals are closed, autoware should not be running. Instead, the same result can be obtained by executing the following command in a python environment: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.transform_rosbag_to_csv(rosbag_dir) ``` Here, `rosbag_dir` represents the rosbag directory. At this time, all CSV files in `rosbag_dir` are automatically deleted first. We move on to an explanation of how the model is trained. If `trained_model_parameter:memory_for_training:use_memory_for_training` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml) is set to `true`, training is performed on models that include LSTM, and if it is set to `false`, training is performed on models that do not include LSTM. When using LSTM, cell states and hidden states are updated based on historical time series data and reflected in the prediction. The paths of the rosbag directories used for training and validation, `dir_0`, `dir_1`, `dir_2`,..., `dir_val_0`, `dir_val_1`, `dir_val_2`,... and the directory `save_dir` where you save the models, the model can be saved in the python environment as follows: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.add_data_from_csv(dir_0, add_mode="as_train") model_trainer.add_data_from_csv(dir_1, add_mode="as_train") model_trainer.add_data_from_csv(dir_2, add_mode="as_train") ... model_trainer.add_data_from_csv(dir_val_0, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_1, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_2, add_mode="as_val") ... model_trainer.get_trained_model() model_trainer.save_models(save_dir) ``` If `add_mode` is not specified or validation data is not added, the training data is split to be used for training and validation. After performing the polynomial regression, the NN can be trained on the residuals as follows: ```python model_trainer.get_trained_model(use_polynomial_reg=True) ``` File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/control/autoware_smart_mpc_trajectory_follower/README.md)

CHANGELOG

Changelog for package autoware_smart_mpc_trajectory_follower

0.47.0 (2025-08-11)

feat: change planning output topic name to /planning/trajectory (#11135)
- change planning output topic name to /planning/trajectory
* style(pre-commit): autofix ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Contributors: Yukihiro Saito

0.46.0 (2025-06-20)

0.45.0 (2025-05-22)

Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
fix(smart_mpc): imperative mood (#10658)
Contributors: TaikiYamada4, Yuxuan Liu

0.44.2 (2025-06-10)

0.44.1 (2025-05-01)

0.44.0 (2025-04-18)

0.43.0 (2025-03-21)

Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
Contributors: Hayato Mizushima, Yutaka Kondo

0.42.0 (2025-03-03)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
Contributors: Fumiya Watanabe, 心刚

0.41.2 (2025-02-19)

chore: bump version to 0.41.1 (#10088)
Contributors: Ryohsuke Mitsudome

0.41.1 (2025-02-10)

0.41.0 (2025-01-29)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat: tier4_debug_msgs changed to autoware_internal_debug_msgs in fil… (#9851)
Contributors: Fumiya Watanabe, Vishal Chauhan

0.40.0 (2024-12-12)

Merge branch 'main' into release-0.40.0
Revert "chore(package.xml): bump version to 0.39.0 (#9587)" This reverts commit c9f0f2688c57b0f657f5c1f28f036a970682e7f5.
fix: fix ticket links in CHANGELOG.rst (#9588)
chore(package.xml): bump version to 0.39.0 (#9587)
- chore(package.xml): bump version to 0.39.0
- fix: fix ticket links in CHANGELOG.rst
* fix: remove unnecessary diff ---------Co-authored-by: Yutaka Kondo <<yutaka.kondo@youtalk.jp>>
fix: fix ticket links in CHANGELOG.rst (#9588)
fix(cpplint): include what you use - control (#9565)
fix(autoware_smart_mpc_trajectory_follower): fix clang-diagnostic-ignored-optimization-argument (#9437)
0.39.0
update changelog

File truncated at 100 lines see the full file

Package Dependencies

Deps	Name
	ament_cmake_auto
	ament_cmake_python
	autoware_cmake
	ros2launch
	ament_cmake_ros
	ament_index_python
	ament_lint_auto
	autoware_lint_common
	autoware_testing
	ros_testing
	autoware_adapi_v1_msgs
	autoware_control_msgs
	autoware_motion_utils
	autoware_planning_msgs
	autoware_system_msgs
	autoware_utils
	autoware_vehicle_cmd_gate
	autoware_vehicle_msgs
	pybind11_vendor
	rclcpp
	rclcpp_components

System Dependencies

Name
python3-scipy
python3-torch

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged `autoware_smart_mpc_trajectory_follower` at Robotics Stack Exchange

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autoware_smart_mpc_trajectory_follower package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter autoware_diagnostic_graph_test_examples tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

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github

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Package Summary

Tags	No category tags.
Version	0.47.0
License	Apache License 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description
Checkout URI	https://github.com/autowarefoundation/autoware_universe.git
VCS Type	git
VCS Version	main
Last Updated	2025-08-16
Dev Status	UNKNOWN
Released	UNRELEASED
Tags	planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Nodes to follow a trajectory by generating control commands using smart mpc

Additional Links

No additional links.

Maintainers

Masayuki Aino
Kosuke Takeuchi
Takamasa Horibe
Takayuki Murooka

Authors

Masayuki Aino

control/autoware_smart_mpc_trajectory_follower/README.md

Smart MPC Trajectory Follower

Smart MPC (Model Predictive Control) is a control algorithm that combines model predictive control and machine learning. While inheriting the advantages of model predictive control, it solves its disadvantage of modeling difficulty with a data-driven method using machine learning.

This technology makes it relatively easy to operate model predictive control, which is expensive to implement, as long as an environment for collecting data can be prepared.

</a> ## Provided features This package provides smart MPC logic for path-following control as well as mechanisms for learning and evaluation. These features are described below. ### Trajectory following control based on iLQR/MPPI The control mode can be selected from "ilqr", "mppi", or "mppi_ilqr", and can be set as `mpc_parameter:system:mode` in [mpc_param.yaml](./autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml). In "mppi_ilqr" mode, the initial value of iLQR is given by the MPPI solution. > [!NOTE] > With the default settings, the performance of "mppi" mode is limited due to an insufficient number of samples. This issue is being addressed with ongoing work to introduce GPU support. To perform a simulation, run the following command: ```bash ros2 launch autoware_launch planning_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit trajectory_follower_mode:=smart_mpc_trajectory_follower ``` > [!NOTE] > When running with the nominal model set in [nominal_param.yaml](./autoware_smart_mpc_trajectory_follower/param/nominal_param.yaml), set `trained_model_parameter:control_application:use_trained_model` to `false` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml). To run using the trained model, set `trained_model_parameter:control_application:use_trained_model` to `true`, but the trained model must have been generated according to the following procedure. ### Training of model and reflection in control To obtain training data, start autoware, perform a drive, and record rosbag data with the following commands. ```bash ros2 bag record /localization/kinematic_state /localization/acceleration /vehicle/status/steering_status /control/command/control_cmd /control/trajectory_follower/control_cmd /control/trajectory_follower/lane_departure_checker_node/debug/deviation/lateral /control/trajectory_follower/lane_departure_checker_node/debug/deviation/yaw /system/operation_mode/state /vehicle/status/control_mode /sensing/imu/imu_data /debug_mpc_x_des /debug_mpc_y_des /debug_mpc_v_des /debug_mpc_yaw_des /debug_mpc_acc_des /debug_mpc_steer_des /debug_mpc_X_des_converted /debug_mpc_x_current /debug_mpc_error_prediction /debug_mpc_max_trajectory_err /debug_mpc_emergency_stop_mode /debug_mpc_goal_stop_mode /debug_mpc_total_ctrl_time /debug_mpc_calc_u_opt_time ``` Move [rosbag2.bash](./autoware_smart_mpc_trajectory_follower/training_and_data_check/rosbag2.bash) to the rosbag directory recorded above and execute the following command on the directory ```bash bash rosbag2.bash ``` This converts rosbag data into CSV format for training models. > [!NOTE] > Note that a large number of terminals are automatically opened at runtime, but they are automatically closed after rosbag data conversion is completed. > From the time you begin this process until all terminals are closed, autoware should not be running. Instead, the same result can be obtained by executing the following command in a python environment: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.transform_rosbag_to_csv(rosbag_dir) ``` Here, `rosbag_dir` represents the rosbag directory. At this time, all CSV files in `rosbag_dir` are automatically deleted first. We move on to an explanation of how the model is trained. If `trained_model_parameter:memory_for_training:use_memory_for_training` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml) is set to `true`, training is performed on models that include LSTM, and if it is set to `false`, training is performed on models that do not include LSTM. When using LSTM, cell states and hidden states are updated based on historical time series data and reflected in the prediction. The paths of the rosbag directories used for training and validation, `dir_0`, `dir_1`, `dir_2`,..., `dir_val_0`, `dir_val_1`, `dir_val_2`,... and the directory `save_dir` where you save the models, the model can be saved in the python environment as follows: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() model_trainer.add_data_from_csv(dir_0, add_mode="as_train") model_trainer.add_data_from_csv(dir_1, add_mode="as_train") model_trainer.add_data_from_csv(dir_2, add_mode="as_train") ... model_trainer.add_data_from_csv(dir_val_0, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_1, add_mode="as_val") model_trainer.add_data_from_csv(dir_val_2, add_mode="as_val") ... model_trainer.get_trained_model() model_trainer.save_models(save_dir) ``` If `add_mode` is not specified or validation data is not added, the training data is split to be used for training and validation. After performing the polynomial regression, the NN can be trained on the residuals as follows: ```python model_trainer.get_trained_model(use_polynomial_reg=True) ``` File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/control/autoware_smart_mpc_trajectory_follower/README.md)

CHANGELOG

Changelog for package autoware_smart_mpc_trajectory_follower

0.47.0 (2025-08-11)

feat: change planning output topic name to /planning/trajectory (#11135)
- change planning output topic name to /planning/trajectory
* style(pre-commit): autofix ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Contributors: Yukihiro Saito

0.46.0 (2025-06-20)

0.45.0 (2025-05-22)

Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
fix(smart_mpc): imperative mood (#10658)
Contributors: TaikiYamada4, Yuxuan Liu

0.44.2 (2025-06-10)

0.44.1 (2025-05-01)

0.44.0 (2025-04-18)

0.43.0 (2025-03-21)

Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
Contributors: Hayato Mizushima, Yutaka Kondo

0.42.0 (2025-03-03)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
Contributors: Fumiya Watanabe, 心刚

0.41.2 (2025-02-19)

chore: bump version to 0.41.1 (#10088)
Contributors: Ryohsuke Mitsudome

0.41.1 (2025-02-10)

0.41.0 (2025-01-29)

Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
feat: tier4_debug_msgs changed to autoware_internal_debug_msgs in fil… (#9851)
Contributors: Fumiya Watanabe, Vishal Chauhan

0.40.0 (2024-12-12)

Merge branch 'main' into release-0.40.0
Revert "chore(package.xml): bump version to 0.39.0 (#9587)" This reverts commit c9f0f2688c57b0f657f5c1f28f036a970682e7f5.
fix: fix ticket links in CHANGELOG.rst (#9588)
chore(package.xml): bump version to 0.39.0 (#9587)
- chore(package.xml): bump version to 0.39.0
- fix: fix ticket links in CHANGELOG.rst
* fix: remove unnecessary diff ---------Co-authored-by: Yutaka Kondo <<yutaka.kondo@youtalk.jp>>
fix: fix ticket links in CHANGELOG.rst (#9588)
fix(cpplint): include what you use - control (#9565)
fix(autoware_smart_mpc_trajectory_follower): fix clang-diagnostic-ignored-optimization-argument (#9437)
0.39.0
update changelog

File truncated at 100 lines see the full file

Package Dependencies

Deps	Name
	ament_cmake_auto
	ament_cmake_python
	autoware_cmake
	ros2launch
	ament_cmake_ros
	ament_index_python
	ament_lint_auto
	autoware_lint_common
	autoware_testing
	ros_testing
	autoware_adapi_v1_msgs
	autoware_control_msgs
	autoware_motion_utils
	autoware_planning_msgs
	autoware_system_msgs
	autoware_utils
	autoware_vehicle_cmd_gate
	autoware_vehicle_msgs
	pybind11_vendor
	rclcpp
	rclcpp_components

System Dependencies

Name
python3-scipy
python3-torch

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged `autoware_smart_mpc_trajectory_follower` at Robotics Stack Exchange

API Docs Browse Code Wiki

autoware_smart_mpc_trajectory_follower package from autoware_universe repo

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Package Summary

Repository Summary

Package Description

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Maintainers

Authors

Smart MPC Trajectory Follower

Changelog for package autoware_smart_mpc_trajectory_follower

0.47.0 (2025-08-11)

0.46.0 (2025-06-20)

0.45.0 (2025-05-22)

0.44.2 (2025-06-10)

0.44.1 (2025-05-01)

0.44.0 (2025-04-18)

0.43.0 (2025-03-21)

0.42.0 (2025-03-03)

0.41.2 (2025-02-19)

0.41.1 (2025-02-10)

0.41.0 (2025-01-29)

0.40.0 (2024-12-12)

Package Dependencies

System Dependencies

Dependant Packages

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Services

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Recent questions tagged autoware_smart_mpc_trajectory_follower at Robotics Stack Exchange

autoware_smart_mpc_trajectory_follower package from autoware_universe repo

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Package Summary

Repository Summary

Package Description

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Smart MPC Trajectory Follower

Changelog for package autoware_smart_mpc_trajectory_follower

0.47.0 (2025-08-11)

0.46.0 (2025-06-20)

0.45.0 (2025-05-22)

0.44.2 (2025-06-10)

0.44.1 (2025-05-01)

0.44.0 (2025-04-18)

0.43.0 (2025-03-21)

0.42.0 (2025-03-03)

0.41.2 (2025-02-19)

0.41.1 (2025-02-10)

0.41.0 (2025-01-29)

0.40.0 (2024-12-12)

Package Dependencies

System Dependencies

Dependant Packages

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Messages

Services

Plugins

Recent questions tagged autoware_smart_mpc_trajectory_follower at Robotics Stack Exchange

autoware_smart_mpc_trajectory_follower package from autoware_universe repo

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Package Summary

Repository Summary

Package Description

Additional Links

Maintainers

Authors

Smart MPC Trajectory Follower

Changelog for package autoware_smart_mpc_trajectory_follower

0.47.0 (2025-08-11)

0.46.0 (2025-06-20)

0.45.0 (2025-05-22)

0.44.2 (2025-06-10)

0.44.1 (2025-05-01)

0.44.0 (2025-04-18)

0.43.0 (2025-03-21)

0.42.0 (2025-03-03)

0.41.2 (2025-02-19)

0.41.1 (2025-02-10)

ROS Distro
github

Recent questions tagged `autoware_smart_mpc_trajectory_follower` at Robotics Stack Exchange

ROS Distro
github

Recent questions tagged `autoware_smart_mpc_trajectory_follower` at Robotics Stack Exchange

ROS Distro
github

Recent questions tagged `autoware_smart_mpc_trajectory_follower` at Robotics Stack Exchange

ROS Distro
github

Recent questions tagged `autoware_smart_mpc_trajectory_follower` at Robotics Stack Exchange

ROS Distro
github

Recent questions tagged `autoware_smart_mpc_trajectory_follower` at Robotics Stack Exchange