用学习方法解决赛车漂移中惯性过渡的稳定控制难题
A Learning-based Planning and Control Framework for Inertia Drift Vehicles
- 基于贝叶斯优化构建规划与控制框架，实现平滑过渡
- 仿真验证在8字形路径上可稳定完成急弯漂移，速度损失小
- 适合自动驾驶赛车、高动态车辆控制研究者参考
惯性漂移是两个相反方向持续漂移阶段之间的过渡动作，对自动驾驶赛车连续急弯导航具有重要价值。然而，该过程对漂移控制器要求极高，需在快速切换侧滑角的同时保持精准路径跟踪。此外，精确漂移控制依赖高保真车辆模型以确定漂移平衡点并预测状态，但强耦合的纵向-横向漂移动力学和不可预测的环境变化常导致模型失准。为此，本文提出一种基于学习的规划与控制框架，采用贝叶斯优化（BO）设计规划逻辑，确保惯性漂移与持续漂移阶段间平滑过渡且速度损失最小；同时利用BO学习性能驱动的控制策略，缓解建模误差，提升系统性能。在8字形参考路径上的仿真结果表明，该框架可实现通过急弯时的平稳稳定惯性漂移。
原文摘要 · Abstract (English)
Inertia drift is a transitional maneuver between two sustained drift stages in opposite directions, which provides valuable insights for navigating consecutive sharp corners for autonomous racing.However, this can be a challenging scenario for the drift controller to handle rapid transitions between opposing sideslip angles while maintaining accurate path tracking. Moreover, accurate drift control depends on a high-fidelity vehicle model to derive drift equilibrium points and predict vehicle states, but this is often compromised by the strongly coupled longitudinal-lateral drift dynamics and unpredictable environmental variations. To address these challenges, this paper proposes a learning-based planning and control framework utilizing Bayesian optimization (BO), which develops a planning logic to ensure a smooth transition and minimal velocity loss between inertia and sustained drift phases. BO is further employed to learn a performance-driven control policy that mitigates modeling errors for enhanced system performance. Simulation results on an 8-shape reference path demonstrate that the proposed framework can achieve smooth and stable inertia drift through sharp corners.
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