Resolving edge-cases in autonomous driving, head-to-head autonomous racing is getting a lot of attention from the industry and academia. In this study, we propose a game-theoretic model predictive control (MPC) approach for head-to-head autonomous racing and data-driven model identification method. For the practical estimation of nonlinear model parameters, we adopted the hyperband algorithm, which is used for neural model training in machine learning. The proposed controller comprises three modules: 1) game-based opponents' trajectory predictor, 2) high-level race strategy planner, and 3) MPC-based low-level controller. The game-based predictor was designed to predict the future trajectories of competitors. Based on the prediction results, the high-level race strategy planner plans several behaviors to respond to various race circumstances. Finally, the MPC-based controller computes the optimal control commands to follow the trajectories. The proposed approach was validated under various racing circumstances in an official simulator of the Indy Autonomous Challenge. The experimental results show that the proposed method can effectively overtake competitors, while driving through the track as quickly as possible without collisions.
FSDP: Fast and Safe Data-Driven Overtaking Trajectory Planning for Head-to-Head Autonomous Racing Competitions
Department of Control Science and Engineering, Zhejiang University（控制科学与工程系，浙江大学）；Center for Project-Based Learning, D-ITET, ETH Zurich（基于项目的学习中心，苏黎世联邦理工学院）
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）
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）
Head-to-Head autonomous racing at the limits of handling in the A2RL challenge
Institute of Automotive Technology, TUM（汽车技术研究所，慕尼黑技术大学）；Chair of Automatic Control, TUM（自动控制教授座，慕尼黑技术大学）；Professorship Autonomous Vehicle Systems, TUM（自主车辆系统教授职位，慕尼黑技术大学）
ForzaETH Race Stack -- Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardware
ETH Zürich（苏黎世联邦理工学院）；Center for Project-Based Learning (PBL), ETH Zürich（苏黎世联邦理工学院项目式学习中心）；Institute for Dynamic Systems and Control (IDSC), ETH Zürich（苏黎世联邦理工学院动态系统与控制研究所）
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）
End2Race: An End-to-End Learning Framework for Multi-Vehicle Autonomous Racing
Department of Civil and Environmental Engineering, University of Michigan（土木与环境工程系，密歇根大学）；University of Michigan Transportation Research Institute（密歇根大学交通研究所）
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））
er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds
University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）；University of Parma（帕尔马大学）；Technology Innovation Institute - Autonomous Robotics Research Center（技术创新研究所自主机器人研究中心）；HIPERT srl（希佩尔特有限公司）；University of Pisa（比萨大学）；ETH Zurich（苏黎世联邦理工学院）
