深度强化学习引导的模型预测控制在自主赛车中防止超车的应用
Deep Reinforcement-Learning-Guided Model Predictive Control for Preventing Overtakes in Autonomous Racing
- University of Michigan（密歇根大学）
- National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究自主赛车防御性阻挡问题，通过分层强化学习引导的模型预测控制框架，将防御转化为空间占用调节问题。在仿真中提升平均超车时间，减少对手前进距离，使车辆有效利用轮胎力，且求解时间短，支持实时高速对抗。
AI中文摘要：
本文研究自主赛车中的防御性阻挡问题，即车辆在接近动态极限行驶时必须防止更快的对手超车。不同于最小化单圈时间，我们通过分层强化学习引导的模型预测控制框架将防御制定为空间占用调节问题。软演员评论家策略层在弗伦内特域中运行以生成几何感知防御参考，作为摩擦约束下的空间正则化嵌入非线性模型预测控制公式。在雷山赛道西段的仿真评估中，该框架将平均超车时间从8.8秒增加到14.6秒，同时显著减少对手的前进距离，还能让车辆利用83.4%的可用轮胎力，平均求解时间为33.3毫秒，支持实时高速对抗交互。
英文摘要：
This paper addresses defensive blocking in autonomous racing, where a vehicle must prevent a faster opponent from overtaking while operating near its dynamic limits. Different from lap-time minimization, we formulate defense as a spatial occupancy regulation problem via a hierarchical reinforcement-learning guided model predictive control framework. A Soft Actor-Critic strategic layer operates in the Frenet domain to generate geometry-aware defensive references, which are embedded into the nonlinear model predictive control formulation as spatial regularization under friction constraints. Evaluated on the Thunderhill West circuit in simulation, the framework increases average overtake time from 8.8 s to 14.6 s while significantly reducing opponent progress. Meanwhile, it allows the vehicle to utilize 83.4% of available tire force. The framework achieves a 33.3 ms mean solve time (13.9 ms std), supporting real-time high-speed adversarial interaction.
