用强化学习让自动驾驶车零样本实现高难度漂移，保持稳定与精准。
Reference-Free Formula Drift with Reinforcement Learning: From Driving Data to Tire Energy-Inspired, Real-World Policies
- 基于轮胎能量吸收机制设计强化学习策略，无需轨迹规划
- 实车测试中跟踪误差低于10厘米，侧滑角达63°仍稳定
- 仅在仿真中训练，即可直接部署到真实车辆上
漂移驾驶——即以受控过度转向状态操作车辆——可使未来自动驾驶汽车在恶劣条件或避障时保持最大控制灵活性。本文研究实时漂移策略，在不依赖昂贵轨迹优化的前提下，将车辆准确引导至目标位置。为此，我们设计了一种强化学习代理，基于轮胎能量吸收理念，能在复杂多变的航点配置下自主漂移，并安全保持在赛道边界内。该代理在基于神经随机微分方程车辆模型（由预收集驾驶数据学习而来）构建的仿真环境中训练完成，实现了零样本部署。在丰田GR Supra和雷克萨斯LC 500上的实验表明，该代理能平稳通过不同航点配置，跟踪误差低至10厘米，同时稳定实现高达63°的侧滑角。
原文摘要 · Abstract (English)
The skill to drift a car--i.e., operate in a state of controlled oversteer like professional drivers--could give future autonomous cars maximum flexibility when they need to retain control in adverse conditions or avoid collisions. We investigate real-time drifting strategies that put the car where needed while bypassing expensive trajectory optimization. To this end, we design a reinforcement learning agent that builds on the concept of tire energy absorption to autonomously drift through changing and complex waypoint configurations while safely staying within track bounds. We achieve zero-shot deployment on the car by training the agent in a simulation environment built on top of a neural stochastic differential equation vehicle model learned from pre-collected driving data. Experiments on a Toyota GR Supra and Lexus LC 500 show that the agent is capable of drifting smoothly through varying waypoint configurations with tracking error as low as 10 cm while stably pushing the vehicles to sideslip angles of up to 63°.
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