用强化学习+模型预测控制，让电动车高速漂移过弯更精准可靠。
High-Speed Cornering Control and Real-Vehicle Deployment for Autonomous Electric Vehicles
- 先用贝塞尔曲线优化路径，再用TD3算法训练漂移策略。
- 实车测试中完成漂移U型转弯和直角转弯，轨迹跟踪误差小于0.15米。
- 首次在消费级电动车上实现强化学习漂移控制，适合自动驾驶动力学研究者。
高速过弯时执行漂移动作对自动驾驶汽车构成重大挑战，但可有效缩短转向时间并提升驾驶动态表现。尽管强化学习（RL）在仿真环境中展现出良好效果，但仿真与现实之间的差异限制了其实际部署。本文提出一种融合轨迹优化与漂移动作的创新控制框架，通过贝塞尔曲线预规划轨迹以增强奖励信号，并在仿真环境中使用双延迟深度确定性策略梯度（TD3）优化控制器。为实现真实车辆部署，采用混合式RL-MPC融合机制，将TD3生成的漂移动作作为模型预测控制器（MPC）的主要输入，由MPC实时提供修正输入以弥合仿真与现实的差距。该方法在消费级电动车型上进行了实车测试，重点验证了漂移U型转弯和漂移直角转弯的表现。实验结果详细记录于论文中，并附有视频证据（https://youtu.be/5wp67FcpfL8）。本研究是首次在消费级电动车上部署并应用基于强化学习的瞬态漂移过弯算法。
原文摘要 · Abstract (English)
Executing drift maneuvers during high-speed cornering presents significant challenges for autonomous vehicles, yet offers the potential to minimize turning time and enhance driving dynamics. While reinforcement learning (RL) has shown promising results in simulated environments, discrepancies between simulations and real-world conditions have limited its practical deployment. This study introduces an innovative control framework that integrates trajectory optimization with drift maneuvers, aiming to improve the algorithm's adaptability for real-vehicle implementation. We leveraged Bezier-based pre-trajectory optimization to enhance rewards and optimize the controller through Twin Delayed Deep Deterministic Policy Gradient (TD3) in a simulated environment. For real-world deployment, we implement a hybrid RL-MPC fusion mechanism, , where TD3-derived maneuvers serve as primary inputs for a Model Predictive Controller (MPC). This integration enables precise real-time tracking of the optimal trajectory, with MPC providing corrective inputs to bridge the gap between simulation and reality. The efficacy of this method is validated through real-vehicle tests on consumer-grade electric vehicles, focusing on drift U-turns and drift right-angle turns. The control outcomes of these real-vehicle tests are thoroughly documented in the paper, supported by supplementary video evidence (https://youtu.be/5wp67FcpfL8). Notably, this study is the first to deploy and apply an RL-based transient drift cornering algorithm on consumer-grade electric vehicles.
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