Autonomous Racing Competitions:
a Review of Platforms, Software Development, and Sustainability Contributions
| DOWNLOAD | DOI: 10.62897/COS2024.2-1.106 |
|
Author: Zalán Demeter, Alireza Falah, Levente Puskás Széchenyi István University - HUMDA Lab Nonprofit Ltd., Hungary zalan.demeter@humda.hu |
Abstract: Motor racing challenges the limits of speed, performance, and control. However, human drivers face physical and mental constraints that autonomous vehicles can overcome using advanced sensors, algorithms, and computing power. This paper reviews the current state of autonomous racing, focusing on key events, platforms, and series such as F1Tenth, the Indy Autonomous Challenge (IAC), and the Abu Dhabi Autonomous Racing League (A2RL). The aim is to assess the development and deployment of autonomous software systems, examining technical chal-lenges related to perception, planning, control, learning, and safety. Additionally, we analyse the scalability of these solutions from simulation to real-world environments and evaluate their contributions to sustainability, including promoting STEM education, advancing vehicle safety, and fostering public acceptance of autonomous technologies. Through this review, we highlight how autonomous racing contributes to both the motorsport community and broader society by accelerating research and innovation.
REFERENCES
Alvarez , Denner N., Feng Z., Fischer D., Gao Y., Harsch L., Herz S., Large N.L., Nguyen B., Rosero C., 2022, The software stack that won the formula student driverless competition. arXiv preprint arXiv:2210.10933.
Betz , Betz T., Fent F., Geisslinger M., Heilmeier A., Hermansdorfer L., Herrmann T., Huch S., Karle P., Lien-kamp M., 2023, Tum autonomous motorsport: An autonomous racing software for the indy autonomous challenge. Journal of Field Robotics, 40(4), 783-809.
Betz , Zheng H., Liniger A., Rosolia U., Karle P., Behl M., Krovi V., Mangharam R., 2022, Autonomous vehi-cles on the edge: A survey on autonomous vehicle racing. IEEE Open Journal of Intelligent Transportation Systems, 3, 458-488.
Coulter , 1992, Implementation of the Pure Pursuit Path Tracking Algorithm. Technical Report, <https://www.ri.cmu.edu/pub_files/pub3/coulter_r_craig_1992_1/coulter_r_craig_1992_1.pdf>, accessed 03.12.2024.
Daza G., Izquierdo R., Martínez L.M., Benderius O., Llorca D.F., 2023, Sim-to-real transfer and reality gap modeling in model predictive control for autonomous driving. Applied Intelligence, 53(10), 12719-12735, DOI: 10.1007/s10489-022-04148-1.
Demeter , Bogdán P., Bogár-Németh Á., Bári G., 2024b, Scalable Supervisory Architecture for Autonomous Race Cars. 2024 IEEE Intelligent Vehicles Symposium (IV) 264–271, DOI: 10.1109/IV55156.2024.10588615.
Demeter Z., Hell M., Hajgató G., 2024a, Lessons Learned from an Autonomous Race Car Competition. 2024 Sustainable Mobility and Transportation Symposium, in press.
Fazekas , Demeter Z., Tóth J., Bogár-Németh Á., Bári G., 2024, Evaluation of Local Planner-Based Stanley Control in Autonomous RC Car Racing Series. 2024 IEEE Intelligent Vehicles Symposium (IV) 252-257, DOI: 10.1109/IV55156.2024.10588629.
Hell , Hajgató G., Bogár-Németh Á., Bári G., 2024, A LiDAR-Based Approach to Autonomous Racing with Model-Free Reinforcement Learning. 2024 IEEE Intelligent Vehicles Symposium (IV) 258-263, DOI: 10.1109-IV55156.2024.10588613.
Hoffmann M., Tomlin C.J., Montemerlo M., Thrun S., 2007, Autonomous Automobile Trajectory Tracking for Off-Road Driving: Controller Design, Experimental Validation and Racing. 2007 American Control Con-ference, 2296-2301, DOI: <10.1109/ACC.2007.4282788>.
Jung , Finazzi A., Seong H., Lee D., Lee S., Kim B., Gang G., Han S., Shim D., 2023. An Autonomous System for Head-to-Head Race: Design, Implementation and Analysis; Team KAIST at the Indy Autonomous Chal-lenge. ArXiv, abs/2303.09463, DOI: 10.48550/arXiv.2303.09463.
Kabzan , Valls M.I., Reijgwart V.J., Hendrikx H.F., Ehmke C., Prajapat M., Bühler A., Gosala N., Gupta M., Sivanesan R., 2020, AMZ driverless: The full autonomous racing system. Journal of Field Robotics, 37(7), 1267-1294.
Manivasagam S., Bârsan I.A., Wang J., Yang Z., Urtasun R., 2023, Towards zero domain gap: A compre-hensive study of realistic lidar simulation for autonomy testing. Proceedings of the IEEE/CVF International Conference on Computer Vision, 8272-8282, DOI: 10.1109/ICCV51070.2023.00760.
O’Kelly , Sukhil V., Abbas H., Harkins J., Kao C., Pant Y.V., Mangharam R., Agarwal D., Behl M., Burgio P., 2019, F1/10: An open-source autonomous cyber-physical platform. arXiv preprint arXiv:1901.08567.
Raji A., Liniger, A., Giove, A., Toschi, A., Musiu, N., Morra, D., Verucchi, M., Caporale, D., Bertogna, M., 2022, Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds. DOI: 10.1109-2022.9922239.
Sauerbeck F., Huch S., Fent F., Karle P., Kulmer D., Betz J., 2023, Learn to See Fast: Lessons Learned From Autonomous Racing on How to Develop Perception Systems. IEEE Access, DOI: 10.1109/AC-2023.3272750.
Srinivasan , Sa I., Zyner A., Reijgwart V., Valls M.I., Siegwart R., 2020, End-to-End Velocity Estima-tion for Autonomous Racing. IEEE Robotics and Automation Letters, 5(4), 6869-6875, DOI: 10.1109-LRA.2020.3016929.
Stahl T., Wischnewski A., Betz J., Lienkamp M., 2019, Multilayer graph-based trajectory planning for race vehicles in dynamic 2019 IEEE intelligent transportation systems conference (ITSC), 3149-3154, DOI: 10.1109/ITSC.2019.8917032.
Toschi , Musiu N., Gatti F., Raji A., Amerotti F., Verucchi M., Bertogna M., 2024, Guess the Drift with LOP-UKF: LiDAR Odometry and Pacejka Model for Real-Time Racecar Sideslip Estimation. arXiv preprint arX-iv:2405.05668.
Trumpp R., Javanmardi E., Nakazato J., Tsukada M., Caccamo M., 2024, RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning. arXiv preprint arX-iv:2403.07129.
Wischnewski A., Herrmann T., Werner F., Lohmann B., 2023, A Tube-MPC Approach to Autonomous Multi-Vehicle Racing on High-Speed Ovals. IEEE Transactions on Intelligent Vehicles 8(1) 368-378, DOI: 1109/TIV.2022.3169986.