TY - GEN
T1 - Winning the 3rd Japan Automotive AI Challenge - Autonomous Racing with the Autoware.Auto Open Source Software Stack
AU - Zang, Zirui
AU - Tumu, Renukanandan
AU - Betz, Johannes
AU - Zheng, Hongrui
AU - Mangharam, Rahul
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The 3rd Japan Automotive AI Challenge was an international online autonomous racing challenge where 164 teams competed in December 2021. This paper outlines the winning strategy to this competition, and the advantages and challenges of using the Autoware.Auto open source autonomous driving platform for multi-agent racing. Our winning approach includes a lane-switching opponent overtaking strategy, a global raceline optimization, and the integration of various tools from Autoware.Auto including a Model-Predictive Controller. We describe the use of perception, planning and control modules for high-speed racing applications and provide experience-based insights on working with Autoware.Auto. While our approach is a rule-based strategy that is suitable for non-interactive opponents, it provides a good reference and benchmark for learning-enabled approaches.
AB - The 3rd Japan Automotive AI Challenge was an international online autonomous racing challenge where 164 teams competed in December 2021. This paper outlines the winning strategy to this competition, and the advantages and challenges of using the Autoware.Auto open source autonomous driving platform for multi-agent racing. Our winning approach includes a lane-switching opponent overtaking strategy, a global raceline optimization, and the integration of various tools from Autoware.Auto including a Model-Predictive Controller. We describe the use of perception, planning and control modules for high-speed racing applications and provide experience-based insights on working with Autoware.Auto. While our approach is a rule-based strategy that is suitable for non-interactive opponents, it provides a good reference and benchmark for learning-enabled approaches.
KW - automobiles
KW - autonomous systems
KW - intelligent vehicles
KW - model predictive control
KW - path planning
UR - http://www.scopus.com/inward/record.url?scp=85135376445&partnerID=8YFLogxK
U2 - 10.1109/IV51971.2022.9827162
DO - 10.1109/IV51971.2022.9827162
M3 - Conference contribution
AN - SCOPUS:85135376445
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 1757
EP - 1764
BT - 2022 IEEE Intelligent Vehicles Symposium, IV 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Intelligent Vehicles Symposium, IV 2022
Y2 - 5 June 2022 through 9 June 2022
ER -