Autonomous racing extends beyond the challenge of controlling a racecar at its physical limits. Professional racers employ strategic maneuvers to outwit other competing opponents to secure victory. While modern control algorithms can achieve human-level performance by computing offline racing lines for single-car scenarios, research on real-time algorithms for multi-car autonomous racing is limited. To bridge this gap, we develop game-theoretic modeling framework that incorporates the competitive aspect of autonomous racing like overtaking and blocking through a novel policy parametrization, while operating the car at its limit. Furthermore, we propose an algorithmic approach to compute the (approximate) Nash equilibrium strategy, which represents the optimal approach in the presence of competing agents. Specifically, we introduce an algorithm inspired by recently introduced framework of dynamic near-potential function, enabling real-time computation of the Nash equilibrium. Our approach comprises two phases: offline and online. During the offline phase, we use simulated racing data to learn a near-potential function that approximates utility changes for agents. This function facilitates the online computation of approximate Nash equilibria by maximizing its value. We evaluate our method in a head-to-head 3-car racing scenario, demonstrating superior performance compared to several existing baselines.
Accelerating Autonomy: Insights from Pro Racers in the Era of Autonomous Racing - An Expert Interview Study
Technical University of Munich（慕尼黑工业大学）；TUM School of Engineering and Design（慕尼黑工业大学工程与设计学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）
Regulation-Aware Game-Theoretic Motion Planning for Autonomous Racing
University of Modena and Reggio Emilia（摩德纳和雷吉奥艾米利亚大学）；University of Bologna（博洛尼亚大学）；University of California, Berkeley（加州大学伯克利分校）；Chalmers University of Technology（查尔姆斯理工大学）
A Hybrid Sampling-Based Trajectory Planner with Game-Theoretic Guidance for Autonomous Racing
Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Technical University of Munich（慕尼黑工业大学工程与设计学院自动驾驶车辆系统教授组）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）
Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室（CSAIL））；Laboratory of Information and Decision Systems (LIDS), Massachusetts Institute of Technology（麻省理工学院信息与决策系统实验室（LIDS））
RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality
Department of Civil Engineering（土木工程系）；Department of Aerospace Engineering（航空航天工程系）；Department of Civil, Environmental, and Geo-Engineering（土木、环境与地球工程系）
Bridging the Gap between Discrete Agent Strategies in Game Theory and Continuous Motion Planning in Dynamic Environments
Department of Electrical and Systems Engineering, University of Pennsylvania（电气与系统工程系，宾夕法尼亚大学）；Department of Mathematics, University of Pennsylvania（数学系，宾夕法尼亚大学）
Hierarchical Control for Head-to-Head Autonomous Racing
Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；School for Engineering of Matter, Transport, and Energy（物质、运输与能源工程学院）；Arizona State University（亚利桑那州立大学）
