RACECAR Dataset

Welcome to the RACECAR dataset!

The RACECAR dataset is the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge during 2021-22 have contributed to this dataset. The dataset spans 11 interesting racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at different speeds. The data is organized and released in both ROS2 and nuScenes format. We have also developed the ROS2-to-nuScenes conversion library to achieve this. The RACECAR data is unique because of the high-speed environment of autonomous racing and is suitable to explore issues regarding localization, object detection and tracking (LiDAR, Radar, and Camera), and mapping that arise at the limits of operation of the autonomous vehicle.

[RACECAR Data Video Demo:]

This repository describes how the data was collected, how to download the data, its format and organization in ROS2 and NuScenes, as well as helper scripts used to parse the dataset, custom ros messages describing GNSS/IMU/Radar data, and a conversion script that converts ros2 bags to nuScenes json files.

Overview

• Data Collection
• Data Usage and Availability
  • Data Usage and Licensce
  • Citation
  • Availability
• Data Organization
• Scenario Description
• Coordinate Conventions
• RACECAR ROS2 - Data Structure
  • Folder Structure
  • Data Processing
  • Topic List
• RACECAR nuScenes - Data Structure
  • Folder Structure
• Tutorials
  • Tutorial 1: Visualization
    • Custom ROS2 Messages
    • RVIZ
  • Tutorial 2: Localization
  • Tutorial 3: nuScenes
• Acknowledgements

Data Collection

The RACECAR dataset is compiled by contributions from several teams, all of whom competed in the inaugural season of the Indy Autonomous Challenge during 2021-22. Nine university teams participated in two races. The first race was held at the Indianapolis Motor Speedway (IMS) track in Indiana, USA in October 2021, and the second race was held at Las Vegas Motor Speedway (LVMS) in January 2022. At IMS, teams reached speeds up to 150 mph on straights and 136 mph in turns, competing in solo vehicle time trials and obstacle avoidance. At LVMS, teams participated in a head-to-head overtaking competition reaching speeds in excess of 150 mph, with the fastest overtake taking place at 170 mph.

The AV-21 Indy Lights vehicle is outfitted with three radars, six pinhole cameras, and three solid- state LiDARs. Each of the sensor modalities covers a 360- degree field of view around the vehicle. For localization, the vehicle is equipped with two sets of high-precision Real-Time Kinematic (RTK) GNSS receivers and IMU.

The nine teams that participated were:

Data Usage and Availability

Data Usage and License

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0). To obtain a copy of this license, see LICENSE-CC-BY-NC-4.0.txt in the archive, visit CreativeCommons.org or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Under the following terms:

Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial — You may not use the material for commercial purposes. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Citation

Please refer to our paper for more information and cite it if you use it in your research.

``` @inproceedings{Kulkarni_2023, title={RACECAR - The Dataset for High-Speed Autonomous Racing}, url={http://dx.doi.org/10.1109/IROS55552.2023.10342053}, DOI={10.1109/iros55552.2023.10342053}, booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, publisher={IEEE}, author={Kulkarni, Amar and Chrosniak, John and Ducote, Emory and Sauerbeck, Florian and Saba, Andrew and Chirimar, Utkarsh and Link, John and Behl, Madhur and Cellina, Marcello}, year={2023},

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