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Head-to-Head autonomous racing at the limits of handling in the A2RL challenge

2026-02-09 · Simon Hoffmann, Simon Sagmeister, Tobias Betz, Joscha Bongard, Sascha Büttner, Dominic Ebner, Daniel Esser, Georg Jank, Sven Goblirsch, Alexander Langmann, Maximilian Leitenstern, Levent Ögretmen, Phillip Pitschi, Ann-Kathrin Schwehn, Cornelius Schröder, Marcel Weinmann, Frederik Werner, Boris Lohmann, Johannes Betz, Markus Lienkamp arxiv

Autonomous racing presents a complex challenge involving multi-agent interactions between vehicles operating at the limit of performance and dynamics. As such, it provides a valuable research and testing environment for advancing autonomous driving technology and improving road safety. This article presents the algorithms and deployment strategies developed by the TUM Autonomous Motorsport team for the inaugural Abu Dhabi Autonomous Racing League (A2RL). We showcase how our software emulates human driving behavior, pushing the limits of vehicle handling and multi-vehicle interactions to win the A2RL. Finally, we highlight the key enablers of our success and share our most significant learnings.

📄 PDF Abstract BibTeX arXiv:2602.08571

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Tasks

Autonomous Driving

Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning

2023-08-25 · Dvij Kalaria, Qin Lin, John M. Dolan

Head-to-head autonomous racing is a challenging problem, as the vehicle needs to operate at the friction or handling limits in order to achieve minimum lap times while also actively looking for strategies to overtake/sta…

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Autonomous Driving

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Autonomous Racing continuous-control Continuous Control Model-based Reinforcement Learning+2

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High-speed, head-to-head autonomous racing presents substantial technical and logistical challenges, including precise localization, rapid perception, dynamic planning, and real-time control-compounded by limited track a…

Autonomous Vehicle Collision Avoidance With Racing Parameterized Deep Reinforcement Learning

2026-04-17 · Shathushan Sivashangaran, Vihaan Dutta, Apoorva Khairnar, Sepideh Gohari 외 arxiv

Road traffic accidents are a leading cause of fatalities worldwide. In the US, human error causes 94% of crashes, resulting in excess of 7,000 pedestrian fatalities and $500 billion in costs annually. Autonomous Vehicles…

Computational Efficiency Reinforcement Learning Autonomous Vehicles Collision Avoidance