Autonomous race driving poses a complex control challenge as vehicles must be operated at the edge of their handling limits to reduce lap times while respecting physical and safety constraints. This paper presents a novel reinforcement learning (RL)-based approach, incorporating the action mapping (AM) mechanism to manage state-dependent input constraints arising from limited tire-road friction. A numerical approximation method is proposed to implement AM, addressing the complex dynamics associated with the friction constraints. The AM mechanism also allows the learned driving policy to be generalized to different friction conditions. Experimental results in our developed race simulator demonstrate that the proposed AM-RL approach achieves superior lap times and better success rates compared to the conventional RL-based approaches. The generalization capability of driving policy with AM is also validated in the experiments.

翻译：自动驾驶赛车提出了一个复杂的控制挑战，因为车辆必须在操控极限边缘运行以缩短单圈时间，同时遵守物理和安全约束。本文提出了一种新颖的基于强化学习（RL）的方法，该方法结合了动作映射（AM）机制来管理由有限轮胎-路面摩擦引起的状态相关输入约束。提出了一种数值近似方法来实现AM，以处理与摩擦约束相关的复杂动力学问题。AM机制还使得学习到的驾驶策略能够泛化到不同的摩擦条件。在我们开发的赛车模拟器中的实验结果表明，与传统的基于RL的方法相比，所提出的AM-RL方法实现了更优的单圈时间和更高的成功率。实验也验证了采用AM的驾驶策略的泛化能力。