Package Summary
| Version | 0.53.0 |
| 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 | 2026-10-23 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Maintainers
- Tao Zhong
- Yoshi Ri
- Shintaro Tomie
- Masato Saeki
- Taekjin Lee
- Masaki Baba
Authors
autoware_traffic_light_fine_detector
Purpose
It is a package for traffic light detection using YOLOX-s.
Training Information
Pretrained Model
The model is based on YOLOX and the pretrained model could be downloaded from here.
Training Data
The model was fine-tuned on around 17,000 TIER IV internal images of Japanese traffic lights.
Trained Onnx model
You can download the ONNX file using these instructions.
Please visit Autoware artifacts documentation for more information.
Inner-workings / Algorithms
Based on the camera image and the global ROI array detected by map_based_detector node, a CNN-based detection method enables highly accurate traffic light detection. If can not detect traffic light, x_offset, y_offset, height and width of output ROI become 0.
ROIs detected from YOLOX will be selected by a combination of expect/rois. At this time, evaluate the whole as ROIs, not just the ROI alone.
Inputs / Outputs
Input
| Name | Type | Description |
|---|---|---|
~/input/image |
sensor_msgs::msg::Image |
The full size camera image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector |
~/expect/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector without any offset, used to select the best detection results |
Output
| Name | Type | Description |
|---|---|---|
~/output/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The detected accurate rois |
~/debug/exe_time_ms |
autoware_internal_debug_msgs::msg::Float32Stamped |
The time taken for inference |
Parameters
| {{ json_to_markdown(“perception/autoware_traffic_light_fine_detector/schema/traffic_light_fine_detector.schema.json”) }} |
Assumptions / Known limits
Reference repositories
YOLOX github repository
Changelog for package autoware_traffic_light_fine_detector
0.53.0 (2026-09-29)
0.52.0 (2026-06-30)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
refactor(autoware_tensorrt_yolox): remove unused public API (#12835) Remove public API symbols that have no caller in the autowarefoundation and tier4 organizations (verified via GitHub code search):
- preprocess.{hpp,cu}: 6 unused exported GPU functions and their kernels (resize_bilinear_gpu, letterbox_gpu, nchw_to_nhwc_gpu, to_float_gpu, resize_bilinear_letterbox_gpu, resize_bilinear_letterbox_nhwc_to_nchw32_gpu); the batch/multi-scale/argmax variants that are actually used are kept
- TrtYoloX::printProfiling and TrtYoloX::initPreprocessBuffer
- the unused cache_dir constructor parameter (documented as "unused variable"); update the call sites in tensorrt_yolox_detector and autoware_traffic_light_fine_detector accordingly Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
refactor(tensorrt_yolox): unify preprocessing on GPU and remove CPU path (#12803) Remove the use_gpu_preprocess option and the CPU preprocessing code path so that preprocessing is always performed on the GPU.
- Drop the use_gpu_preprocess constructor argument and use_gpu_preprocess_ flag
- Delete the CPU-only preprocess(), multiScalePreprocess() and getMaskImage()
- Remove now-dead members input_h_ and segmentation_out_prob_h_
- Drop the preprocess_on_gpu ROS parameter, config struct member, param files, JSON schema entries and the test override
- Remove unused includes (<fstream>, <optional>) from the touched files
- Update autoware_traffic_light_fine_detector for the new constructor signature Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
Contributors: Takahisa Ishikawa, github-actions
0.51.0 (2026-05-01)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
feat: default artifact paths to ~/autoware_data/ml_models (#12523) feat(launches,configs): default artifact paths to ~/autoware_data/ml_models Roll every per-package [data_path]{.title-ref} / [model_path]{.title-ref} launch-arg default from [$(env HOME)/autoware_data[/...]]{.title-ref} to [$(env HOME)/autoware_data/ml_models[/...]]{.title-ref} so standalone universe launches resolve artifacts under the new [~/autoware_data/ml_models/]{.title-ref} layout (autowarefoundation/autoware#7068). When invoked through autoware_launch the parent overrides cascade and already pin the new root (autowarefoundation/autoware_launch#1835); this commit closes the gap for users who launch a perception / localization / sensing / planning component directly with [ros2 launch <pkg>]{.title-ref}. 22 launch files updated (one-line default change each):
- e2e/autoware_tensorrt_vad/launch/vad_carla_tiny.launch.xml
- localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml
- perception/autoware_bevfusion/launch/bevfusion.launch.xml
- perception/autoware_camera_streampetr/launch/streampetr.launch.xml
- perception/autoware_image_projection_based_fusion/launch/pointpainting_fusion.launch.xml
- perception/autoware_lidar_apollo_instance_segmentation/launch/lidar_apollo_instance_segmentation.launch.xml
- perception/autoware_lidar_centerpoint/launch/lidar_centerpoint.launch.xml
- perception/autoware_lidar_frnet/launch/lidar_frnet.launch.xml
- perception/autoware_lidar_transfusion/launch/lidar_transfusion.launch.xml
- perception/autoware_ptv3/launch/ptv3.launch.xml
- perception/autoware_shape_estimation/launch/shape_estimation.launch.xml
- perception/autoware_simpl_prediction/launch/simpl.launch.xml
- perception/autoware_tensorrt_bevdet/launch/tensorrt_bevdet.launch.xml
- perception/autoware_tensorrt_bevformer/launch/bevformer.launch.xml
- perception/autoware_tensorrt_yolox/launch/{yolox_traffic_light_detector,yolox_tiny,yolox_s_plus_opt}.launch.xml
- perception/autoware_traffic_light_classifier/launch/{car,pedestrian}_traffic_light_classifier.launch.xml
- perception/autoware_traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml
- planning/autoware_diffusion_planner/launch/diffusion_planner.launch.xml
- sensing/autoware_calibration_status_classifier/launch/calibration_status_classifier.launch.xml Drive-by README and test fixes:
- e2e/autoware_tensorrt_vad/{README.md,docs/design.md}: also
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
| Name | Deps |
|---|---|
| autoware_perception_launch |
Launch files
- launch/traffic_light_fine_detector.launch.xml
-
- data_path [default: $(env HOME)/autoware_data/ml_models]
- input/image [default: /image_raw]
- input/rois [default: /traffic_light_map_based_detector/output/rois]
- expect/rois [default: ~/expect/rois]
- output/rois [default: ~/output/rois]
- param_path [default: $(find-pkg-share autoware_traffic_light_fine_detector)/config/traffic_light_fine_detector.param.yaml]
- model_path [default: $(var data_path)/traffic_light_fine_detector/tlr_car_ped_yolox_s_batch_6.onnx]
- label_path [default: $(var data_path)/traffic_light_fine_detector/tlr_labels.txt]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged autoware_traffic_light_fine_detector at Robotics Stack Exchange
Package Summary
| Version | 0.53.0 |
| 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 | 2026-10-23 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Maintainers
- Tao Zhong
- Yoshi Ri
- Shintaro Tomie
- Masato Saeki
- Taekjin Lee
- Masaki Baba
Authors
autoware_traffic_light_fine_detector
Purpose
It is a package for traffic light detection using YOLOX-s.
Training Information
Pretrained Model
The model is based on YOLOX and the pretrained model could be downloaded from here.
Training Data
The model was fine-tuned on around 17,000 TIER IV internal images of Japanese traffic lights.
Trained Onnx model
You can download the ONNX file using these instructions.
Please visit Autoware artifacts documentation for more information.
Inner-workings / Algorithms
Based on the camera image and the global ROI array detected by map_based_detector node, a CNN-based detection method enables highly accurate traffic light detection. If can not detect traffic light, x_offset, y_offset, height and width of output ROI become 0.
ROIs detected from YOLOX will be selected by a combination of expect/rois. At this time, evaluate the whole as ROIs, not just the ROI alone.
Inputs / Outputs
Input
| Name | Type | Description |
|---|---|---|
~/input/image |
sensor_msgs::msg::Image |
The full size camera image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector |
~/expect/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector without any offset, used to select the best detection results |
Output
| Name | Type | Description |
|---|---|---|
~/output/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The detected accurate rois |
~/debug/exe_time_ms |
autoware_internal_debug_msgs::msg::Float32Stamped |
The time taken for inference |
Parameters
| {{ json_to_markdown(“perception/autoware_traffic_light_fine_detector/schema/traffic_light_fine_detector.schema.json”) }} |
Assumptions / Known limits
Reference repositories
YOLOX github repository
Changelog for package autoware_traffic_light_fine_detector
0.53.0 (2026-09-29)
0.52.0 (2026-06-30)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
refactor(autoware_tensorrt_yolox): remove unused public API (#12835) Remove public API symbols that have no caller in the autowarefoundation and tier4 organizations (verified via GitHub code search):
- preprocess.{hpp,cu}: 6 unused exported GPU functions and their kernels (resize_bilinear_gpu, letterbox_gpu, nchw_to_nhwc_gpu, to_float_gpu, resize_bilinear_letterbox_gpu, resize_bilinear_letterbox_nhwc_to_nchw32_gpu); the batch/multi-scale/argmax variants that are actually used are kept
- TrtYoloX::printProfiling and TrtYoloX::initPreprocessBuffer
- the unused cache_dir constructor parameter (documented as "unused variable"); update the call sites in tensorrt_yolox_detector and autoware_traffic_light_fine_detector accordingly Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
refactor(tensorrt_yolox): unify preprocessing on GPU and remove CPU path (#12803) Remove the use_gpu_preprocess option and the CPU preprocessing code path so that preprocessing is always performed on the GPU.
- Drop the use_gpu_preprocess constructor argument and use_gpu_preprocess_ flag
- Delete the CPU-only preprocess(), multiScalePreprocess() and getMaskImage()
- Remove now-dead members input_h_ and segmentation_out_prob_h_
- Drop the preprocess_on_gpu ROS parameter, config struct member, param files, JSON schema entries and the test override
- Remove unused includes (<fstream>, <optional>) from the touched files
- Update autoware_traffic_light_fine_detector for the new constructor signature Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
Contributors: Takahisa Ishikawa, github-actions
0.51.0 (2026-05-01)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
feat: default artifact paths to ~/autoware_data/ml_models (#12523) feat(launches,configs): default artifact paths to ~/autoware_data/ml_models Roll every per-package [data_path]{.title-ref} / [model_path]{.title-ref} launch-arg default from [$(env HOME)/autoware_data[/...]]{.title-ref} to [$(env HOME)/autoware_data/ml_models[/...]]{.title-ref} so standalone universe launches resolve artifacts under the new [~/autoware_data/ml_models/]{.title-ref} layout (autowarefoundation/autoware#7068). When invoked through autoware_launch the parent overrides cascade and already pin the new root (autowarefoundation/autoware_launch#1835); this commit closes the gap for users who launch a perception / localization / sensing / planning component directly with [ros2 launch <pkg>]{.title-ref}. 22 launch files updated (one-line default change each):
- e2e/autoware_tensorrt_vad/launch/vad_carla_tiny.launch.xml
- localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml
- perception/autoware_bevfusion/launch/bevfusion.launch.xml
- perception/autoware_camera_streampetr/launch/streampetr.launch.xml
- perception/autoware_image_projection_based_fusion/launch/pointpainting_fusion.launch.xml
- perception/autoware_lidar_apollo_instance_segmentation/launch/lidar_apollo_instance_segmentation.launch.xml
- perception/autoware_lidar_centerpoint/launch/lidar_centerpoint.launch.xml
- perception/autoware_lidar_frnet/launch/lidar_frnet.launch.xml
- perception/autoware_lidar_transfusion/launch/lidar_transfusion.launch.xml
- perception/autoware_ptv3/launch/ptv3.launch.xml
- perception/autoware_shape_estimation/launch/shape_estimation.launch.xml
- perception/autoware_simpl_prediction/launch/simpl.launch.xml
- perception/autoware_tensorrt_bevdet/launch/tensorrt_bevdet.launch.xml
- perception/autoware_tensorrt_bevformer/launch/bevformer.launch.xml
- perception/autoware_tensorrt_yolox/launch/{yolox_traffic_light_detector,yolox_tiny,yolox_s_plus_opt}.launch.xml
- perception/autoware_traffic_light_classifier/launch/{car,pedestrian}_traffic_light_classifier.launch.xml
- perception/autoware_traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml
- planning/autoware_diffusion_planner/launch/diffusion_planner.launch.xml
- sensing/autoware_calibration_status_classifier/launch/calibration_status_classifier.launch.xml Drive-by README and test fixes:
- e2e/autoware_tensorrt_vad/{README.md,docs/design.md}: also
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
| Name | Deps |
|---|---|
| autoware_perception_launch |
Launch files
- launch/traffic_light_fine_detector.launch.xml
-
- data_path [default: $(env HOME)/autoware_data/ml_models]
- input/image [default: /image_raw]
- input/rois [default: /traffic_light_map_based_detector/output/rois]
- expect/rois [default: ~/expect/rois]
- output/rois [default: ~/output/rois]
- param_path [default: $(find-pkg-share autoware_traffic_light_fine_detector)/config/traffic_light_fine_detector.param.yaml]
- model_path [default: $(var data_path)/traffic_light_fine_detector/tlr_car_ped_yolox_s_batch_6.onnx]
- label_path [default: $(var data_path)/traffic_light_fine_detector/tlr_labels.txt]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged autoware_traffic_light_fine_detector at Robotics Stack Exchange
Package Summary
| Version | 0.53.0 |
| 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 | 2026-10-23 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Maintainers
- Tao Zhong
- Yoshi Ri
- Shintaro Tomie
- Masato Saeki
- Taekjin Lee
- Masaki Baba
Authors
autoware_traffic_light_fine_detector
Purpose
It is a package for traffic light detection using YOLOX-s.
Training Information
Pretrained Model
The model is based on YOLOX and the pretrained model could be downloaded from here.
Training Data
The model was fine-tuned on around 17,000 TIER IV internal images of Japanese traffic lights.
Trained Onnx model
You can download the ONNX file using these instructions.
Please visit Autoware artifacts documentation for more information.
Inner-workings / Algorithms
Based on the camera image and the global ROI array detected by map_based_detector node, a CNN-based detection method enables highly accurate traffic light detection. If can not detect traffic light, x_offset, y_offset, height and width of output ROI become 0.
ROIs detected from YOLOX will be selected by a combination of expect/rois. At this time, evaluate the whole as ROIs, not just the ROI alone.
Inputs / Outputs
Input
| Name | Type | Description |
|---|---|---|
~/input/image |
sensor_msgs::msg::Image |
The full size camera image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector |
~/expect/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector without any offset, used to select the best detection results |
Output
| Name | Type | Description |
|---|---|---|
~/output/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The detected accurate rois |
~/debug/exe_time_ms |
autoware_internal_debug_msgs::msg::Float32Stamped |
The time taken for inference |
Parameters
| {{ json_to_markdown(“perception/autoware_traffic_light_fine_detector/schema/traffic_light_fine_detector.schema.json”) }} |
Assumptions / Known limits
Reference repositories
YOLOX github repository
Changelog for package autoware_traffic_light_fine_detector
0.53.0 (2026-09-29)
0.52.0 (2026-06-30)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
refactor(autoware_tensorrt_yolox): remove unused public API (#12835) Remove public API symbols that have no caller in the autowarefoundation and tier4 organizations (verified via GitHub code search):
- preprocess.{hpp,cu}: 6 unused exported GPU functions and their kernels (resize_bilinear_gpu, letterbox_gpu, nchw_to_nhwc_gpu, to_float_gpu, resize_bilinear_letterbox_gpu, resize_bilinear_letterbox_nhwc_to_nchw32_gpu); the batch/multi-scale/argmax variants that are actually used are kept
- TrtYoloX::printProfiling and TrtYoloX::initPreprocessBuffer
- the unused cache_dir constructor parameter (documented as "unused variable"); update the call sites in tensorrt_yolox_detector and autoware_traffic_light_fine_detector accordingly Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
refactor(tensorrt_yolox): unify preprocessing on GPU and remove CPU path (#12803) Remove the use_gpu_preprocess option and the CPU preprocessing code path so that preprocessing is always performed on the GPU.
- Drop the use_gpu_preprocess constructor argument and use_gpu_preprocess_ flag
- Delete the CPU-only preprocess(), multiScalePreprocess() and getMaskImage()
- Remove now-dead members input_h_ and segmentation_out_prob_h_
- Drop the preprocess_on_gpu ROS parameter, config struct member, param files, JSON schema entries and the test override
- Remove unused includes (<fstream>, <optional>) from the touched files
- Update autoware_traffic_light_fine_detector for the new constructor signature Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
Contributors: Takahisa Ishikawa, github-actions
0.51.0 (2026-05-01)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
feat: default artifact paths to ~/autoware_data/ml_models (#12523) feat(launches,configs): default artifact paths to ~/autoware_data/ml_models Roll every per-package [data_path]{.title-ref} / [model_path]{.title-ref} launch-arg default from [$(env HOME)/autoware_data[/...]]{.title-ref} to [$(env HOME)/autoware_data/ml_models[/...]]{.title-ref} so standalone universe launches resolve artifacts under the new [~/autoware_data/ml_models/]{.title-ref} layout (autowarefoundation/autoware#7068). When invoked through autoware_launch the parent overrides cascade and already pin the new root (autowarefoundation/autoware_launch#1835); this commit closes the gap for users who launch a perception / localization / sensing / planning component directly with [ros2 launch <pkg>]{.title-ref}. 22 launch files updated (one-line default change each):
- e2e/autoware_tensorrt_vad/launch/vad_carla_tiny.launch.xml
- localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml
- perception/autoware_bevfusion/launch/bevfusion.launch.xml
- perception/autoware_camera_streampetr/launch/streampetr.launch.xml
- perception/autoware_image_projection_based_fusion/launch/pointpainting_fusion.launch.xml
- perception/autoware_lidar_apollo_instance_segmentation/launch/lidar_apollo_instance_segmentation.launch.xml
- perception/autoware_lidar_centerpoint/launch/lidar_centerpoint.launch.xml
- perception/autoware_lidar_frnet/launch/lidar_frnet.launch.xml
- perception/autoware_lidar_transfusion/launch/lidar_transfusion.launch.xml
- perception/autoware_ptv3/launch/ptv3.launch.xml
- perception/autoware_shape_estimation/launch/shape_estimation.launch.xml
- perception/autoware_simpl_prediction/launch/simpl.launch.xml
- perception/autoware_tensorrt_bevdet/launch/tensorrt_bevdet.launch.xml
- perception/autoware_tensorrt_bevformer/launch/bevformer.launch.xml
- perception/autoware_tensorrt_yolox/launch/{yolox_traffic_light_detector,yolox_tiny,yolox_s_plus_opt}.launch.xml
- perception/autoware_traffic_light_classifier/launch/{car,pedestrian}_traffic_light_classifier.launch.xml
- perception/autoware_traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml
- planning/autoware_diffusion_planner/launch/diffusion_planner.launch.xml
- sensing/autoware_calibration_status_classifier/launch/calibration_status_classifier.launch.xml Drive-by README and test fixes:
- e2e/autoware_tensorrt_vad/{README.md,docs/design.md}: also
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
| Name | Deps |
|---|---|
| autoware_perception_launch |
Launch files
- launch/traffic_light_fine_detector.launch.xml
-
- data_path [default: $(env HOME)/autoware_data/ml_models]
- input/image [default: /image_raw]
- input/rois [default: /traffic_light_map_based_detector/output/rois]
- expect/rois [default: ~/expect/rois]
- output/rois [default: ~/output/rois]
- param_path [default: $(find-pkg-share autoware_traffic_light_fine_detector)/config/traffic_light_fine_detector.param.yaml]
- model_path [default: $(var data_path)/traffic_light_fine_detector/tlr_car_ped_yolox_s_batch_6.onnx]
- label_path [default: $(var data_path)/traffic_light_fine_detector/tlr_labels.txt]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged autoware_traffic_light_fine_detector at Robotics Stack Exchange
Package Summary
| Version | 0.53.0 |
| 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 | 2026-10-23 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Maintainers
- Tao Zhong
- Yoshi Ri
- Shintaro Tomie
- Masato Saeki
- Taekjin Lee
- Masaki Baba
Authors
autoware_traffic_light_fine_detector
Purpose
It is a package for traffic light detection using YOLOX-s.
Training Information
Pretrained Model
The model is based on YOLOX and the pretrained model could be downloaded from here.
Training Data
The model was fine-tuned on around 17,000 TIER IV internal images of Japanese traffic lights.
Trained Onnx model
You can download the ONNX file using these instructions.
Please visit Autoware artifacts documentation for more information.
Inner-workings / Algorithms
Based on the camera image and the global ROI array detected by map_based_detector node, a CNN-based detection method enables highly accurate traffic light detection. If can not detect traffic light, x_offset, y_offset, height and width of output ROI become 0.
ROIs detected from YOLOX will be selected by a combination of expect/rois. At this time, evaluate the whole as ROIs, not just the ROI alone.
Inputs / Outputs
Input
| Name | Type | Description |
|---|---|---|
~/input/image |
sensor_msgs::msg::Image |
The full size camera image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector |
~/expect/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector without any offset, used to select the best detection results |
Output
| Name | Type | Description |
|---|---|---|
~/output/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The detected accurate rois |
~/debug/exe_time_ms |
autoware_internal_debug_msgs::msg::Float32Stamped |
The time taken for inference |
Parameters
| {{ json_to_markdown(“perception/autoware_traffic_light_fine_detector/schema/traffic_light_fine_detector.schema.json”) }} |
Assumptions / Known limits
Reference repositories
YOLOX github repository
Changelog for package autoware_traffic_light_fine_detector
0.53.0 (2026-09-29)
0.52.0 (2026-06-30)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
refactor(autoware_tensorrt_yolox): remove unused public API (#12835) Remove public API symbols that have no caller in the autowarefoundation and tier4 organizations (verified via GitHub code search):
- preprocess.{hpp,cu}: 6 unused exported GPU functions and their kernels (resize_bilinear_gpu, letterbox_gpu, nchw_to_nhwc_gpu, to_float_gpu, resize_bilinear_letterbox_gpu, resize_bilinear_letterbox_nhwc_to_nchw32_gpu); the batch/multi-scale/argmax variants that are actually used are kept
- TrtYoloX::printProfiling and TrtYoloX::initPreprocessBuffer
- the unused cache_dir constructor parameter (documented as "unused variable"); update the call sites in tensorrt_yolox_detector and autoware_traffic_light_fine_detector accordingly Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
refactor(tensorrt_yolox): unify preprocessing on GPU and remove CPU path (#12803) Remove the use_gpu_preprocess option and the CPU preprocessing code path so that preprocessing is always performed on the GPU.
- Drop the use_gpu_preprocess constructor argument and use_gpu_preprocess_ flag
- Delete the CPU-only preprocess(), multiScalePreprocess() and getMaskImage()
- Remove now-dead members input_h_ and segmentation_out_prob_h_
- Drop the preprocess_on_gpu ROS parameter, config struct member, param files, JSON schema entries and the test override
- Remove unused includes (<fstream>, <optional>) from the touched files
- Update autoware_traffic_light_fine_detector for the new constructor signature Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
Contributors: Takahisa Ishikawa, github-actions
0.51.0 (2026-05-01)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
feat: default artifact paths to ~/autoware_data/ml_models (#12523) feat(launches,configs): default artifact paths to ~/autoware_data/ml_models Roll every per-package [data_path]{.title-ref} / [model_path]{.title-ref} launch-arg default from [$(env HOME)/autoware_data[/...]]{.title-ref} to [$(env HOME)/autoware_data/ml_models[/...]]{.title-ref} so standalone universe launches resolve artifacts under the new [~/autoware_data/ml_models/]{.title-ref} layout (autowarefoundation/autoware#7068). When invoked through autoware_launch the parent overrides cascade and already pin the new root (autowarefoundation/autoware_launch#1835); this commit closes the gap for users who launch a perception / localization / sensing / planning component directly with [ros2 launch <pkg>]{.title-ref}. 22 launch files updated (one-line default change each):
- e2e/autoware_tensorrt_vad/launch/vad_carla_tiny.launch.xml
- localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml
- perception/autoware_bevfusion/launch/bevfusion.launch.xml
- perception/autoware_camera_streampetr/launch/streampetr.launch.xml
- perception/autoware_image_projection_based_fusion/launch/pointpainting_fusion.launch.xml
- perception/autoware_lidar_apollo_instance_segmentation/launch/lidar_apollo_instance_segmentation.launch.xml
- perception/autoware_lidar_centerpoint/launch/lidar_centerpoint.launch.xml
- perception/autoware_lidar_frnet/launch/lidar_frnet.launch.xml
- perception/autoware_lidar_transfusion/launch/lidar_transfusion.launch.xml
- perception/autoware_ptv3/launch/ptv3.launch.xml
- perception/autoware_shape_estimation/launch/shape_estimation.launch.xml
- perception/autoware_simpl_prediction/launch/simpl.launch.xml
- perception/autoware_tensorrt_bevdet/launch/tensorrt_bevdet.launch.xml
- perception/autoware_tensorrt_bevformer/launch/bevformer.launch.xml
- perception/autoware_tensorrt_yolox/launch/{yolox_traffic_light_detector,yolox_tiny,yolox_s_plus_opt}.launch.xml
- perception/autoware_traffic_light_classifier/launch/{car,pedestrian}_traffic_light_classifier.launch.xml
- perception/autoware_traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml
- planning/autoware_diffusion_planner/launch/diffusion_planner.launch.xml
- sensing/autoware_calibration_status_classifier/launch/calibration_status_classifier.launch.xml Drive-by README and test fixes:
- e2e/autoware_tensorrt_vad/{README.md,docs/design.md}: also
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
| Name | Deps |
|---|---|
| autoware_perception_launch |
Launch files
- launch/traffic_light_fine_detector.launch.xml
-
- data_path [default: $(env HOME)/autoware_data/ml_models]
- input/image [default: /image_raw]
- input/rois [default: /traffic_light_map_based_detector/output/rois]
- expect/rois [default: ~/expect/rois]
- output/rois [default: ~/output/rois]
- param_path [default: $(find-pkg-share autoware_traffic_light_fine_detector)/config/traffic_light_fine_detector.param.yaml]
- model_path [default: $(var data_path)/traffic_light_fine_detector/tlr_car_ped_yolox_s_batch_6.onnx]
- label_path [default: $(var data_path)/traffic_light_fine_detector/tlr_labels.txt]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged autoware_traffic_light_fine_detector at Robotics Stack Exchange
Package Summary
| Version | 0.53.0 |
| 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 | 2026-10-23 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Maintainers
- Tao Zhong
- Yoshi Ri
- Shintaro Tomie
- Masato Saeki
- Taekjin Lee
- Masaki Baba
Authors
autoware_traffic_light_fine_detector
Purpose
It is a package for traffic light detection using YOLOX-s.
Training Information
Pretrained Model
The model is based on YOLOX and the pretrained model could be downloaded from here.
Training Data
The model was fine-tuned on around 17,000 TIER IV internal images of Japanese traffic lights.
Trained Onnx model
You can download the ONNX file using these instructions.
Please visit Autoware artifacts documentation for more information.
Inner-workings / Algorithms
Based on the camera image and the global ROI array detected by map_based_detector node, a CNN-based detection method enables highly accurate traffic light detection. If can not detect traffic light, x_offset, y_offset, height and width of output ROI become 0.
ROIs detected from YOLOX will be selected by a combination of expect/rois. At this time, evaluate the whole as ROIs, not just the ROI alone.
Inputs / Outputs
Input
| Name | Type | Description |
|---|---|---|
~/input/image |
sensor_msgs::msg::Image |
The full size camera image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector |
~/expect/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector without any offset, used to select the best detection results |
Output
| Name | Type | Description |
|---|---|---|
~/output/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The detected accurate rois |
~/debug/exe_time_ms |
autoware_internal_debug_msgs::msg::Float32Stamped |
The time taken for inference |
Parameters
| {{ json_to_markdown(“perception/autoware_traffic_light_fine_detector/schema/traffic_light_fine_detector.schema.json”) }} |
Assumptions / Known limits
Reference repositories
YOLOX github repository
Changelog for package autoware_traffic_light_fine_detector
0.53.0 (2026-09-29)
0.52.0 (2026-06-30)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
refactor(autoware_tensorrt_yolox): remove unused public API (#12835) Remove public API symbols that have no caller in the autowarefoundation and tier4 organizations (verified via GitHub code search):
- preprocess.{hpp,cu}: 6 unused exported GPU functions and their kernels (resize_bilinear_gpu, letterbox_gpu, nchw_to_nhwc_gpu, to_float_gpu, resize_bilinear_letterbox_gpu, resize_bilinear_letterbox_nhwc_to_nchw32_gpu); the batch/multi-scale/argmax variants that are actually used are kept
- TrtYoloX::printProfiling and TrtYoloX::initPreprocessBuffer
- the unused cache_dir constructor parameter (documented as "unused variable"); update the call sites in tensorrt_yolox_detector and autoware_traffic_light_fine_detector accordingly Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
refactor(tensorrt_yolox): unify preprocessing on GPU and remove CPU path (#12803) Remove the use_gpu_preprocess option and the CPU preprocessing code path so that preprocessing is always performed on the GPU.
- Drop the use_gpu_preprocess constructor argument and use_gpu_preprocess_ flag
- Delete the CPU-only preprocess(), multiScalePreprocess() and getMaskImage()
- Remove now-dead members input_h_ and segmentation_out_prob_h_
- Drop the preprocess_on_gpu ROS parameter, config struct member, param files, JSON schema entries and the test override
- Remove unused includes (<fstream>, <optional>) from the touched files
- Update autoware_traffic_light_fine_detector for the new constructor signature Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
Contributors: Takahisa Ishikawa, github-actions
0.51.0 (2026-05-01)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
feat: default artifact paths to ~/autoware_data/ml_models (#12523) feat(launches,configs): default artifact paths to ~/autoware_data/ml_models Roll every per-package [data_path]{.title-ref} / [model_path]{.title-ref} launch-arg default from [$(env HOME)/autoware_data[/...]]{.title-ref} to [$(env HOME)/autoware_data/ml_models[/...]]{.title-ref} so standalone universe launches resolve artifacts under the new [~/autoware_data/ml_models/]{.title-ref} layout (autowarefoundation/autoware#7068). When invoked through autoware_launch the parent overrides cascade and already pin the new root (autowarefoundation/autoware_launch#1835); this commit closes the gap for users who launch a perception / localization / sensing / planning component directly with [ros2 launch <pkg>]{.title-ref}. 22 launch files updated (one-line default change each):
- e2e/autoware_tensorrt_vad/launch/vad_carla_tiny.launch.xml
- localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml
- perception/autoware_bevfusion/launch/bevfusion.launch.xml
- perception/autoware_camera_streampetr/launch/streampetr.launch.xml
- perception/autoware_image_projection_based_fusion/launch/pointpainting_fusion.launch.xml
- perception/autoware_lidar_apollo_instance_segmentation/launch/lidar_apollo_instance_segmentation.launch.xml
- perception/autoware_lidar_centerpoint/launch/lidar_centerpoint.launch.xml
- perception/autoware_lidar_frnet/launch/lidar_frnet.launch.xml
- perception/autoware_lidar_transfusion/launch/lidar_transfusion.launch.xml
- perception/autoware_ptv3/launch/ptv3.launch.xml
- perception/autoware_shape_estimation/launch/shape_estimation.launch.xml
- perception/autoware_simpl_prediction/launch/simpl.launch.xml
- perception/autoware_tensorrt_bevdet/launch/tensorrt_bevdet.launch.xml
- perception/autoware_tensorrt_bevformer/launch/bevformer.launch.xml
- perception/autoware_tensorrt_yolox/launch/{yolox_traffic_light_detector,yolox_tiny,yolox_s_plus_opt}.launch.xml
- perception/autoware_traffic_light_classifier/launch/{car,pedestrian}_traffic_light_classifier.launch.xml
- perception/autoware_traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml
- planning/autoware_diffusion_planner/launch/diffusion_planner.launch.xml
- sensing/autoware_calibration_status_classifier/launch/calibration_status_classifier.launch.xml Drive-by README and test fixes:
- e2e/autoware_tensorrt_vad/{README.md,docs/design.md}: also
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
| Name | Deps |
|---|---|
| autoware_perception_launch |
Launch files
- launch/traffic_light_fine_detector.launch.xml
-
- data_path [default: $(env HOME)/autoware_data/ml_models]
- input/image [default: /image_raw]
- input/rois [default: /traffic_light_map_based_detector/output/rois]
- expect/rois [default: ~/expect/rois]
- output/rois [default: ~/output/rois]
- param_path [default: $(find-pkg-share autoware_traffic_light_fine_detector)/config/traffic_light_fine_detector.param.yaml]
- model_path [default: $(var data_path)/traffic_light_fine_detector/tlr_car_ped_yolox_s_batch_6.onnx]
- label_path [default: $(var data_path)/traffic_light_fine_detector/tlr_labels.txt]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged autoware_traffic_light_fine_detector at Robotics Stack Exchange
Package Summary
| Version | 0.53.0 |
| 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 | 2026-10-23 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Maintainers
- Tao Zhong
- Yoshi Ri
- Shintaro Tomie
- Masato Saeki
- Taekjin Lee
- Masaki Baba
Authors
autoware_traffic_light_fine_detector
Purpose
It is a package for traffic light detection using YOLOX-s.
Training Information
Pretrained Model
The model is based on YOLOX and the pretrained model could be downloaded from here.
Training Data
The model was fine-tuned on around 17,000 TIER IV internal images of Japanese traffic lights.
Trained Onnx model
You can download the ONNX file using these instructions.
Please visit Autoware artifacts documentation for more information.
Inner-workings / Algorithms
Based on the camera image and the global ROI array detected by map_based_detector node, a CNN-based detection method enables highly accurate traffic light detection. If can not detect traffic light, x_offset, y_offset, height and width of output ROI become 0.
ROIs detected from YOLOX will be selected by a combination of expect/rois. At this time, evaluate the whole as ROIs, not just the ROI alone.
Inputs / Outputs
Input
| Name | Type | Description |
|---|---|---|
~/input/image |
sensor_msgs::msg::Image |
The full size camera image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector |
~/expect/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector without any offset, used to select the best detection results |
Output
| Name | Type | Description |
|---|---|---|
~/output/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The detected accurate rois |
~/debug/exe_time_ms |
autoware_internal_debug_msgs::msg::Float32Stamped |
The time taken for inference |
Parameters
| {{ json_to_markdown(“perception/autoware_traffic_light_fine_detector/schema/traffic_light_fine_detector.schema.json”) }} |
Assumptions / Known limits
Reference repositories
YOLOX github repository
Changelog for package autoware_traffic_light_fine_detector
0.53.0 (2026-09-29)
0.52.0 (2026-06-30)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
refactor(autoware_tensorrt_yolox): remove unused public API (#12835) Remove public API symbols that have no caller in the autowarefoundation and tier4 organizations (verified via GitHub code search):
- preprocess.{hpp,cu}: 6 unused exported GPU functions and their kernels (resize_bilinear_gpu, letterbox_gpu, nchw_to_nhwc_gpu, to_float_gpu, resize_bilinear_letterbox_gpu, resize_bilinear_letterbox_nhwc_to_nchw32_gpu); the batch/multi-scale/argmax variants that are actually used are kept
- TrtYoloX::printProfiling and TrtYoloX::initPreprocessBuffer
- the unused cache_dir constructor parameter (documented as "unused variable"); update the call sites in tensorrt_yolox_detector and autoware_traffic_light_fine_detector accordingly Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
refactor(tensorrt_yolox): unify preprocessing on GPU and remove CPU path (#12803) Remove the use_gpu_preprocess option and the CPU preprocessing code path so that preprocessing is always performed on the GPU.
- Drop the use_gpu_preprocess constructor argument and use_gpu_preprocess_ flag
- Delete the CPU-only preprocess(), multiScalePreprocess() and getMaskImage()
- Remove now-dead members input_h_ and segmentation_out_prob_h_
- Drop the preprocess_on_gpu ROS parameter, config struct member, param files, JSON schema entries and the test override
- Remove unused includes (<fstream>, <optional>) from the touched files
- Update autoware_traffic_light_fine_detector for the new constructor signature Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
Contributors: Takahisa Ishikawa, github-actions
0.51.0 (2026-05-01)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
feat: default artifact paths to ~/autoware_data/ml_models (#12523) feat(launches,configs): default artifact paths to ~/autoware_data/ml_models Roll every per-package [data_path]{.title-ref} / [model_path]{.title-ref} launch-arg default from [$(env HOME)/autoware_data[/...]]{.title-ref} to [$(env HOME)/autoware_data/ml_models[/...]]{.title-ref} so standalone universe launches resolve artifacts under the new [~/autoware_data/ml_models/]{.title-ref} layout (autowarefoundation/autoware#7068). When invoked through autoware_launch the parent overrides cascade and already pin the new root (autowarefoundation/autoware_launch#1835); this commit closes the gap for users who launch a perception / localization / sensing / planning component directly with [ros2 launch <pkg>]{.title-ref}. 22 launch files updated (one-line default change each):
- e2e/autoware_tensorrt_vad/launch/vad_carla_tiny.launch.xml
- localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml
- perception/autoware_bevfusion/launch/bevfusion.launch.xml
- perception/autoware_camera_streampetr/launch/streampetr.launch.xml
- perception/autoware_image_projection_based_fusion/launch/pointpainting_fusion.launch.xml
- perception/autoware_lidar_apollo_instance_segmentation/launch/lidar_apollo_instance_segmentation.launch.xml
- perception/autoware_lidar_centerpoint/launch/lidar_centerpoint.launch.xml
- perception/autoware_lidar_frnet/launch/lidar_frnet.launch.xml
- perception/autoware_lidar_transfusion/launch/lidar_transfusion.launch.xml
- perception/autoware_ptv3/launch/ptv3.launch.xml
- perception/autoware_shape_estimation/launch/shape_estimation.launch.xml
- perception/autoware_simpl_prediction/launch/simpl.launch.xml
- perception/autoware_tensorrt_bevdet/launch/tensorrt_bevdet.launch.xml
- perception/autoware_tensorrt_bevformer/launch/bevformer.launch.xml
- perception/autoware_tensorrt_yolox/launch/{yolox_traffic_light_detector,yolox_tiny,yolox_s_plus_opt}.launch.xml
- perception/autoware_traffic_light_classifier/launch/{car,pedestrian}_traffic_light_classifier.launch.xml
- perception/autoware_traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml
- planning/autoware_diffusion_planner/launch/diffusion_planner.launch.xml
- sensing/autoware_calibration_status_classifier/launch/calibration_status_classifier.launch.xml Drive-by README and test fixes:
- e2e/autoware_tensorrt_vad/{README.md,docs/design.md}: also
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
| Name | Deps |
|---|---|
| autoware_perception_launch |
Launch files
- launch/traffic_light_fine_detector.launch.xml
-
- data_path [default: $(env HOME)/autoware_data/ml_models]
- input/image [default: /image_raw]
- input/rois [default: /traffic_light_map_based_detector/output/rois]
- expect/rois [default: ~/expect/rois]
- output/rois [default: ~/output/rois]
- param_path [default: $(find-pkg-share autoware_traffic_light_fine_detector)/config/traffic_light_fine_detector.param.yaml]
- model_path [default: $(var data_path)/traffic_light_fine_detector/tlr_car_ped_yolox_s_batch_6.onnx]
- label_path [default: $(var data_path)/traffic_light_fine_detector/tlr_labels.txt]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged autoware_traffic_light_fine_detector at Robotics Stack Exchange
Package Summary
| Version | 0.53.0 |
| 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 | 2026-10-23 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Maintainers
- Tao Zhong
- Yoshi Ri
- Shintaro Tomie
- Masato Saeki
- Taekjin Lee
- Masaki Baba
Authors
autoware_traffic_light_fine_detector
Purpose
It is a package for traffic light detection using YOLOX-s.
Training Information
Pretrained Model
The model is based on YOLOX and the pretrained model could be downloaded from here.
Training Data
The model was fine-tuned on around 17,000 TIER IV internal images of Japanese traffic lights.
Trained Onnx model
You can download the ONNX file using these instructions.
Please visit Autoware artifacts documentation for more information.
Inner-workings / Algorithms
Based on the camera image and the global ROI array detected by map_based_detector node, a CNN-based detection method enables highly accurate traffic light detection. If can not detect traffic light, x_offset, y_offset, height and width of output ROI become 0.
ROIs detected from YOLOX will be selected by a combination of expect/rois. At this time, evaluate the whole as ROIs, not just the ROI alone.
Inputs / Outputs
Input
| Name | Type | Description |
|---|---|---|
~/input/image |
sensor_msgs::msg::Image |
The full size camera image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector |
~/expect/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector without any offset, used to select the best detection results |
Output
| Name | Type | Description |
|---|---|---|
~/output/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The detected accurate rois |
~/debug/exe_time_ms |
autoware_internal_debug_msgs::msg::Float32Stamped |
The time taken for inference |
Parameters
| {{ json_to_markdown(“perception/autoware_traffic_light_fine_detector/schema/traffic_light_fine_detector.schema.json”) }} |
Assumptions / Known limits
Reference repositories
YOLOX github repository
Changelog for package autoware_traffic_light_fine_detector
0.53.0 (2026-09-29)
0.52.0 (2026-06-30)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
refactor(autoware_tensorrt_yolox): remove unused public API (#12835) Remove public API symbols that have no caller in the autowarefoundation and tier4 organizations (verified via GitHub code search):
- preprocess.{hpp,cu}: 6 unused exported GPU functions and their kernels (resize_bilinear_gpu, letterbox_gpu, nchw_to_nhwc_gpu, to_float_gpu, resize_bilinear_letterbox_gpu, resize_bilinear_letterbox_nhwc_to_nchw32_gpu); the batch/multi-scale/argmax variants that are actually used are kept
- TrtYoloX::printProfiling and TrtYoloX::initPreprocessBuffer
- the unused cache_dir constructor parameter (documented as "unused variable"); update the call sites in tensorrt_yolox_detector and autoware_traffic_light_fine_detector accordingly Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
refactor(tensorrt_yolox): unify preprocessing on GPU and remove CPU path (#12803) Remove the use_gpu_preprocess option and the CPU preprocessing code path so that preprocessing is always performed on the GPU.
- Drop the use_gpu_preprocess constructor argument and use_gpu_preprocess_ flag
- Delete the CPU-only preprocess(), multiScalePreprocess() and getMaskImage()
- Remove now-dead members input_h_ and segmentation_out_prob_h_
- Drop the preprocess_on_gpu ROS parameter, config struct member, param files, JSON schema entries and the test override
- Remove unused includes (<fstream>, <optional>) from the touched files
- Update autoware_traffic_light_fine_detector for the new constructor signature Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
Contributors: Takahisa Ishikawa, github-actions
0.51.0 (2026-05-01)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
feat: default artifact paths to ~/autoware_data/ml_models (#12523) feat(launches,configs): default artifact paths to ~/autoware_data/ml_models Roll every per-package [data_path]{.title-ref} / [model_path]{.title-ref} launch-arg default from [$(env HOME)/autoware_data[/...]]{.title-ref} to [$(env HOME)/autoware_data/ml_models[/...]]{.title-ref} so standalone universe launches resolve artifacts under the new [~/autoware_data/ml_models/]{.title-ref} layout (autowarefoundation/autoware#7068). When invoked through autoware_launch the parent overrides cascade and already pin the new root (autowarefoundation/autoware_launch#1835); this commit closes the gap for users who launch a perception / localization / sensing / planning component directly with [ros2 launch <pkg>]{.title-ref}. 22 launch files updated (one-line default change each):
- e2e/autoware_tensorrt_vad/launch/vad_carla_tiny.launch.xml
- localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml
- perception/autoware_bevfusion/launch/bevfusion.launch.xml
- perception/autoware_camera_streampetr/launch/streampetr.launch.xml
- perception/autoware_image_projection_based_fusion/launch/pointpainting_fusion.launch.xml
- perception/autoware_lidar_apollo_instance_segmentation/launch/lidar_apollo_instance_segmentation.launch.xml
- perception/autoware_lidar_centerpoint/launch/lidar_centerpoint.launch.xml
- perception/autoware_lidar_frnet/launch/lidar_frnet.launch.xml
- perception/autoware_lidar_transfusion/launch/lidar_transfusion.launch.xml
- perception/autoware_ptv3/launch/ptv3.launch.xml
- perception/autoware_shape_estimation/launch/shape_estimation.launch.xml
- perception/autoware_simpl_prediction/launch/simpl.launch.xml
- perception/autoware_tensorrt_bevdet/launch/tensorrt_bevdet.launch.xml
- perception/autoware_tensorrt_bevformer/launch/bevformer.launch.xml
- perception/autoware_tensorrt_yolox/launch/{yolox_traffic_light_detector,yolox_tiny,yolox_s_plus_opt}.launch.xml
- perception/autoware_traffic_light_classifier/launch/{car,pedestrian}_traffic_light_classifier.launch.xml
- perception/autoware_traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml
- planning/autoware_diffusion_planner/launch/diffusion_planner.launch.xml
- sensing/autoware_calibration_status_classifier/launch/calibration_status_classifier.launch.xml Drive-by README and test fixes:
- e2e/autoware_tensorrt_vad/{README.md,docs/design.md}: also
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
| Name | Deps |
|---|---|
| autoware_perception_launch |
Launch files
- launch/traffic_light_fine_detector.launch.xml
-
- data_path [default: $(env HOME)/autoware_data/ml_models]
- input/image [default: /image_raw]
- input/rois [default: /traffic_light_map_based_detector/output/rois]
- expect/rois [default: ~/expect/rois]
- output/rois [default: ~/output/rois]
- param_path [default: $(find-pkg-share autoware_traffic_light_fine_detector)/config/traffic_light_fine_detector.param.yaml]
- model_path [default: $(var data_path)/traffic_light_fine_detector/tlr_car_ped_yolox_s_batch_6.onnx]
- label_path [default: $(var data_path)/traffic_light_fine_detector/tlr_labels.txt]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged autoware_traffic_light_fine_detector at Robotics Stack Exchange
Package Summary
| Version | 0.53.0 |
| 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 | 2026-10-23 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Maintainers
- Tao Zhong
- Yoshi Ri
- Shintaro Tomie
- Masato Saeki
- Taekjin Lee
- Masaki Baba
Authors
autoware_traffic_light_fine_detector
Purpose
It is a package for traffic light detection using YOLOX-s.
Training Information
Pretrained Model
The model is based on YOLOX and the pretrained model could be downloaded from here.
Training Data
The model was fine-tuned on around 17,000 TIER IV internal images of Japanese traffic lights.
Trained Onnx model
You can download the ONNX file using these instructions.
Please visit Autoware artifacts documentation for more information.
Inner-workings / Algorithms
Based on the camera image and the global ROI array detected by map_based_detector node, a CNN-based detection method enables highly accurate traffic light detection. If can not detect traffic light, x_offset, y_offset, height and width of output ROI become 0.
ROIs detected from YOLOX will be selected by a combination of expect/rois. At this time, evaluate the whole as ROIs, not just the ROI alone.
Inputs / Outputs
Input
| Name | Type | Description |
|---|---|---|
~/input/image |
sensor_msgs::msg::Image |
The full size camera image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector |
~/expect/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector without any offset, used to select the best detection results |
Output
| Name | Type | Description |
|---|---|---|
~/output/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The detected accurate rois |
~/debug/exe_time_ms |
autoware_internal_debug_msgs::msg::Float32Stamped |
The time taken for inference |
Parameters
| {{ json_to_markdown(“perception/autoware_traffic_light_fine_detector/schema/traffic_light_fine_detector.schema.json”) }} |
Assumptions / Known limits
Reference repositories
YOLOX github repository
Changelog for package autoware_traffic_light_fine_detector
0.53.0 (2026-09-29)
0.52.0 (2026-06-30)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
refactor(autoware_tensorrt_yolox): remove unused public API (#12835) Remove public API symbols that have no caller in the autowarefoundation and tier4 organizations (verified via GitHub code search):
- preprocess.{hpp,cu}: 6 unused exported GPU functions and their kernels (resize_bilinear_gpu, letterbox_gpu, nchw_to_nhwc_gpu, to_float_gpu, resize_bilinear_letterbox_gpu, resize_bilinear_letterbox_nhwc_to_nchw32_gpu); the batch/multi-scale/argmax variants that are actually used are kept
- TrtYoloX::printProfiling and TrtYoloX::initPreprocessBuffer
- the unused cache_dir constructor parameter (documented as "unused variable"); update the call sites in tensorrt_yolox_detector and autoware_traffic_light_fine_detector accordingly Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
refactor(tensorrt_yolox): unify preprocessing on GPU and remove CPU path (#12803) Remove the use_gpu_preprocess option and the CPU preprocessing code path so that preprocessing is always performed on the GPU.
- Drop the use_gpu_preprocess constructor argument and use_gpu_preprocess_ flag
- Delete the CPU-only preprocess(), multiScalePreprocess() and getMaskImage()
- Remove now-dead members input_h_ and segmentation_out_prob_h_
- Drop the preprocess_on_gpu ROS parameter, config struct member, param files, JSON schema entries and the test override
- Remove unused includes (<fstream>, <optional>) from the touched files
- Update autoware_traffic_light_fine_detector for the new constructor signature Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
Contributors: Takahisa Ishikawa, github-actions
0.51.0 (2026-05-01)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
feat: default artifact paths to ~/autoware_data/ml_models (#12523) feat(launches,configs): default artifact paths to ~/autoware_data/ml_models Roll every per-package [data_path]{.title-ref} / [model_path]{.title-ref} launch-arg default from [$(env HOME)/autoware_data[/...]]{.title-ref} to [$(env HOME)/autoware_data/ml_models[/...]]{.title-ref} so standalone universe launches resolve artifacts under the new [~/autoware_data/ml_models/]{.title-ref} layout (autowarefoundation/autoware#7068). When invoked through autoware_launch the parent overrides cascade and already pin the new root (autowarefoundation/autoware_launch#1835); this commit closes the gap for users who launch a perception / localization / sensing / planning component directly with [ros2 launch <pkg>]{.title-ref}. 22 launch files updated (one-line default change each):
- e2e/autoware_tensorrt_vad/launch/vad_carla_tiny.launch.xml
- localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml
- perception/autoware_bevfusion/launch/bevfusion.launch.xml
- perception/autoware_camera_streampetr/launch/streampetr.launch.xml
- perception/autoware_image_projection_based_fusion/launch/pointpainting_fusion.launch.xml
- perception/autoware_lidar_apollo_instance_segmentation/launch/lidar_apollo_instance_segmentation.launch.xml
- perception/autoware_lidar_centerpoint/launch/lidar_centerpoint.launch.xml
- perception/autoware_lidar_frnet/launch/lidar_frnet.launch.xml
- perception/autoware_lidar_transfusion/launch/lidar_transfusion.launch.xml
- perception/autoware_ptv3/launch/ptv3.launch.xml
- perception/autoware_shape_estimation/launch/shape_estimation.launch.xml
- perception/autoware_simpl_prediction/launch/simpl.launch.xml
- perception/autoware_tensorrt_bevdet/launch/tensorrt_bevdet.launch.xml
- perception/autoware_tensorrt_bevformer/launch/bevformer.launch.xml
- perception/autoware_tensorrt_yolox/launch/{yolox_traffic_light_detector,yolox_tiny,yolox_s_plus_opt}.launch.xml
- perception/autoware_traffic_light_classifier/launch/{car,pedestrian}_traffic_light_classifier.launch.xml
- perception/autoware_traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml
- planning/autoware_diffusion_planner/launch/diffusion_planner.launch.xml
- sensing/autoware_calibration_status_classifier/launch/calibration_status_classifier.launch.xml Drive-by README and test fixes:
- e2e/autoware_tensorrt_vad/{README.md,docs/design.md}: also
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
| Name | Deps |
|---|---|
| autoware_perception_launch |
Launch files
- launch/traffic_light_fine_detector.launch.xml
-
- data_path [default: $(env HOME)/autoware_data/ml_models]
- input/image [default: /image_raw]
- input/rois [default: /traffic_light_map_based_detector/output/rois]
- expect/rois [default: ~/expect/rois]
- output/rois [default: ~/output/rois]
- param_path [default: $(find-pkg-share autoware_traffic_light_fine_detector)/config/traffic_light_fine_detector.param.yaml]
- model_path [default: $(var data_path)/traffic_light_fine_detector/tlr_car_ped_yolox_s_batch_6.onnx]
- label_path [default: $(var data_path)/traffic_light_fine_detector/tlr_labels.txt]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged autoware_traffic_light_fine_detector at Robotics Stack Exchange
Package Summary
| Version | 0.53.0 |
| 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 | 2026-10-23 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Maintainers
- Tao Zhong
- Yoshi Ri
- Shintaro Tomie
- Masato Saeki
- Taekjin Lee
- Masaki Baba
Authors
autoware_traffic_light_fine_detector
Purpose
It is a package for traffic light detection using YOLOX-s.
Training Information
Pretrained Model
The model is based on YOLOX and the pretrained model could be downloaded from here.
Training Data
The model was fine-tuned on around 17,000 TIER IV internal images of Japanese traffic lights.
Trained Onnx model
You can download the ONNX file using these instructions.
Please visit Autoware artifacts documentation for more information.
Inner-workings / Algorithms
Based on the camera image and the global ROI array detected by map_based_detector node, a CNN-based detection method enables highly accurate traffic light detection. If can not detect traffic light, x_offset, y_offset, height and width of output ROI become 0.
ROIs detected from YOLOX will be selected by a combination of expect/rois. At this time, evaluate the whole as ROIs, not just the ROI alone.
Inputs / Outputs
Input
| Name | Type | Description |
|---|---|---|
~/input/image |
sensor_msgs::msg::Image |
The full size camera image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector |
~/expect/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector without any offset, used to select the best detection results |
Output
| Name | Type | Description |
|---|---|---|
~/output/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The detected accurate rois |
~/debug/exe_time_ms |
autoware_internal_debug_msgs::msg::Float32Stamped |
The time taken for inference |
Parameters
| {{ json_to_markdown(“perception/autoware_traffic_light_fine_detector/schema/traffic_light_fine_detector.schema.json”) }} |
Assumptions / Known limits
Reference repositories
YOLOX github repository
Changelog for package autoware_traffic_light_fine_detector
0.53.0 (2026-09-29)
0.52.0 (2026-06-30)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
refactor(autoware_tensorrt_yolox): remove unused public API (#12835) Remove public API symbols that have no caller in the autowarefoundation and tier4 organizations (verified via GitHub code search):
- preprocess.{hpp,cu}: 6 unused exported GPU functions and their kernels (resize_bilinear_gpu, letterbox_gpu, nchw_to_nhwc_gpu, to_float_gpu, resize_bilinear_letterbox_gpu, resize_bilinear_letterbox_nhwc_to_nchw32_gpu); the batch/multi-scale/argmax variants that are actually used are kept
- TrtYoloX::printProfiling and TrtYoloX::initPreprocessBuffer
- the unused cache_dir constructor parameter (documented as "unused variable"); update the call sites in tensorrt_yolox_detector and autoware_traffic_light_fine_detector accordingly Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
refactor(tensorrt_yolox): unify preprocessing on GPU and remove CPU path (#12803) Remove the use_gpu_preprocess option and the CPU preprocessing code path so that preprocessing is always performed on the GPU.
- Drop the use_gpu_preprocess constructor argument and use_gpu_preprocess_ flag
- Delete the CPU-only preprocess(), multiScalePreprocess() and getMaskImage()
- Remove now-dead members input_h_ and segmentation_out_prob_h_
- Drop the preprocess_on_gpu ROS parameter, config struct member, param files, JSON schema entries and the test override
- Remove unused includes (<fstream>, <optional>) from the touched files
- Update autoware_traffic_light_fine_detector for the new constructor signature Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
Contributors: Takahisa Ishikawa, github-actions
0.51.0 (2026-05-01)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
feat: default artifact paths to ~/autoware_data/ml_models (#12523) feat(launches,configs): default artifact paths to ~/autoware_data/ml_models Roll every per-package [data_path]{.title-ref} / [model_path]{.title-ref} launch-arg default from [$(env HOME)/autoware_data[/...]]{.title-ref} to [$(env HOME)/autoware_data/ml_models[/...]]{.title-ref} so standalone universe launches resolve artifacts under the new [~/autoware_data/ml_models/]{.title-ref} layout (autowarefoundation/autoware#7068). When invoked through autoware_launch the parent overrides cascade and already pin the new root (autowarefoundation/autoware_launch#1835); this commit closes the gap for users who launch a perception / localization / sensing / planning component directly with [ros2 launch <pkg>]{.title-ref}. 22 launch files updated (one-line default change each):
- e2e/autoware_tensorrt_vad/launch/vad_carla_tiny.launch.xml
- localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml
- perception/autoware_bevfusion/launch/bevfusion.launch.xml
- perception/autoware_camera_streampetr/launch/streampetr.launch.xml
- perception/autoware_image_projection_based_fusion/launch/pointpainting_fusion.launch.xml
- perception/autoware_lidar_apollo_instance_segmentation/launch/lidar_apollo_instance_segmentation.launch.xml
- perception/autoware_lidar_centerpoint/launch/lidar_centerpoint.launch.xml
- perception/autoware_lidar_frnet/launch/lidar_frnet.launch.xml
- perception/autoware_lidar_transfusion/launch/lidar_transfusion.launch.xml
- perception/autoware_ptv3/launch/ptv3.launch.xml
- perception/autoware_shape_estimation/launch/shape_estimation.launch.xml
- perception/autoware_simpl_prediction/launch/simpl.launch.xml
- perception/autoware_tensorrt_bevdet/launch/tensorrt_bevdet.launch.xml
- perception/autoware_tensorrt_bevformer/launch/bevformer.launch.xml
- perception/autoware_tensorrt_yolox/launch/{yolox_traffic_light_detector,yolox_tiny,yolox_s_plus_opt}.launch.xml
- perception/autoware_traffic_light_classifier/launch/{car,pedestrian}_traffic_light_classifier.launch.xml
- perception/autoware_traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml
- planning/autoware_diffusion_planner/launch/diffusion_planner.launch.xml
- sensing/autoware_calibration_status_classifier/launch/calibration_status_classifier.launch.xml Drive-by README and test fixes:
- e2e/autoware_tensorrt_vad/{README.md,docs/design.md}: also
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
| Name | Deps |
|---|---|
| autoware_perception_launch |
Launch files
- launch/traffic_light_fine_detector.launch.xml
-
- data_path [default: $(env HOME)/autoware_data/ml_models]
- input/image [default: /image_raw]
- input/rois [default: /traffic_light_map_based_detector/output/rois]
- expect/rois [default: ~/expect/rois]
- output/rois [default: ~/output/rois]
- param_path [default: $(find-pkg-share autoware_traffic_light_fine_detector)/config/traffic_light_fine_detector.param.yaml]
- model_path [default: $(var data_path)/traffic_light_fine_detector/tlr_car_ped_yolox_s_batch_6.onnx]
- label_path [default: $(var data_path)/traffic_light_fine_detector/tlr_labels.txt]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged autoware_traffic_light_fine_detector at Robotics Stack Exchange
Package Summary
| Version | 0.53.0 |
| 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 | 2026-10-23 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Maintainers
- Tao Zhong
- Yoshi Ri
- Shintaro Tomie
- Masato Saeki
- Taekjin Lee
- Masaki Baba
Authors
autoware_traffic_light_fine_detector
Purpose
It is a package for traffic light detection using YOLOX-s.
Training Information
Pretrained Model
The model is based on YOLOX and the pretrained model could be downloaded from here.
Training Data
The model was fine-tuned on around 17,000 TIER IV internal images of Japanese traffic lights.
Trained Onnx model
You can download the ONNX file using these instructions.
Please visit Autoware artifacts documentation for more information.
Inner-workings / Algorithms
Based on the camera image and the global ROI array detected by map_based_detector node, a CNN-based detection method enables highly accurate traffic light detection. If can not detect traffic light, x_offset, y_offset, height and width of output ROI become 0.
ROIs detected from YOLOX will be selected by a combination of expect/rois. At this time, evaluate the whole as ROIs, not just the ROI alone.
Inputs / Outputs
Input
| Name | Type | Description |
|---|---|---|
~/input/image |
sensor_msgs::msg::Image |
The full size camera image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector |
~/expect/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector without any offset, used to select the best detection results |
Output
| Name | Type | Description |
|---|---|---|
~/output/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The detected accurate rois |
~/debug/exe_time_ms |
autoware_internal_debug_msgs::msg::Float32Stamped |
The time taken for inference |
Parameters
| {{ json_to_markdown(“perception/autoware_traffic_light_fine_detector/schema/traffic_light_fine_detector.schema.json”) }} |
Assumptions / Known limits
Reference repositories
YOLOX github repository
Changelog for package autoware_traffic_light_fine_detector
0.53.0 (2026-09-29)
0.52.0 (2026-06-30)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
refactor(autoware_tensorrt_yolox): remove unused public API (#12835) Remove public API symbols that have no caller in the autowarefoundation and tier4 organizations (verified via GitHub code search):
- preprocess.{hpp,cu}: 6 unused exported GPU functions and their kernels (resize_bilinear_gpu, letterbox_gpu, nchw_to_nhwc_gpu, to_float_gpu, resize_bilinear_letterbox_gpu, resize_bilinear_letterbox_nhwc_to_nchw32_gpu); the batch/multi-scale/argmax variants that are actually used are kept
- TrtYoloX::printProfiling and TrtYoloX::initPreprocessBuffer
- the unused cache_dir constructor parameter (documented as "unused variable"); update the call sites in tensorrt_yolox_detector and autoware_traffic_light_fine_detector accordingly Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
refactor(tensorrt_yolox): unify preprocessing on GPU and remove CPU path (#12803) Remove the use_gpu_preprocess option and the CPU preprocessing code path so that preprocessing is always performed on the GPU.
- Drop the use_gpu_preprocess constructor argument and use_gpu_preprocess_ flag
- Delete the CPU-only preprocess(), multiScalePreprocess() and getMaskImage()
- Remove now-dead members input_h_ and segmentation_out_prob_h_
- Drop the preprocess_on_gpu ROS parameter, config struct member, param files, JSON schema entries and the test override
- Remove unused includes (<fstream>, <optional>) from the touched files
- Update autoware_traffic_light_fine_detector for the new constructor signature Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
-
Contributors: Takahisa Ishikawa, github-actions
0.51.0 (2026-05-01)
-
Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
-
feat: default artifact paths to ~/autoware_data/ml_models (#12523) feat(launches,configs): default artifact paths to ~/autoware_data/ml_models Roll every per-package [data_path]{.title-ref} / [model_path]{.title-ref} launch-arg default from [$(env HOME)/autoware_data[/...]]{.title-ref} to [$(env HOME)/autoware_data/ml_models[/...]]{.title-ref} so standalone universe launches resolve artifacts under the new [~/autoware_data/ml_models/]{.title-ref} layout (autowarefoundation/autoware#7068). When invoked through autoware_launch the parent overrides cascade and already pin the new root (autowarefoundation/autoware_launch#1835); this commit closes the gap for users who launch a perception / localization / sensing / planning component directly with [ros2 launch <pkg>]{.title-ref}. 22 launch files updated (one-line default change each):
- e2e/autoware_tensorrt_vad/launch/vad_carla_tiny.launch.xml
- localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml
- perception/autoware_bevfusion/launch/bevfusion.launch.xml
- perception/autoware_camera_streampetr/launch/streampetr.launch.xml
- perception/autoware_image_projection_based_fusion/launch/pointpainting_fusion.launch.xml
- perception/autoware_lidar_apollo_instance_segmentation/launch/lidar_apollo_instance_segmentation.launch.xml
- perception/autoware_lidar_centerpoint/launch/lidar_centerpoint.launch.xml
- perception/autoware_lidar_frnet/launch/lidar_frnet.launch.xml
- perception/autoware_lidar_transfusion/launch/lidar_transfusion.launch.xml
- perception/autoware_ptv3/launch/ptv3.launch.xml
- perception/autoware_shape_estimation/launch/shape_estimation.launch.xml
- perception/autoware_simpl_prediction/launch/simpl.launch.xml
- perception/autoware_tensorrt_bevdet/launch/tensorrt_bevdet.launch.xml
- perception/autoware_tensorrt_bevformer/launch/bevformer.launch.xml
- perception/autoware_tensorrt_yolox/launch/{yolox_traffic_light_detector,yolox_tiny,yolox_s_plus_opt}.launch.xml
- perception/autoware_traffic_light_classifier/launch/{car,pedestrian}_traffic_light_classifier.launch.xml
- perception/autoware_traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml
- planning/autoware_diffusion_planner/launch/diffusion_planner.launch.xml
- sensing/autoware_calibration_status_classifier/launch/calibration_status_classifier.launch.xml Drive-by README and test fixes:
- e2e/autoware_tensorrt_vad/{README.md,docs/design.md}: also
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Package Dependencies
System Dependencies
Dependant Packages
| Name | Deps |
|---|---|
| autoware_perception_launch |
Launch files
- launch/traffic_light_fine_detector.launch.xml
-
- data_path [default: $(env HOME)/autoware_data/ml_models]
- input/image [default: /image_raw]
- input/rois [default: /traffic_light_map_based_detector/output/rois]
- expect/rois [default: ~/expect/rois]
- output/rois [default: ~/output/rois]
- param_path [default: $(find-pkg-share autoware_traffic_light_fine_detector)/config/traffic_light_fine_detector.param.yaml]
- model_path [default: $(var data_path)/traffic_light_fine_detector/tlr_car_ped_yolox_s_batch_6.onnx]
- label_path [default: $(var data_path)/traffic_light_fine_detector/tlr_labels.txt]
- build_only [default: false]