用强化学习动态调整规划参数，让自动驾驶赛车更安全更快。
Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving
- 用RL代理实时切换轨迹规划的权重，实现行为自适应
- 仿真中零碰撞，超车时间减少60%优于传统静态方法
- 适合需要兼顾安全与性能的高速自动驾驶场景
基于采样的轨迹规划器广泛用于高速自动驾驶，因其能快速生成平滑且满足动力学可行性的轨迹。然而，其行为通常由人工调参的静态权重成本函数决定，导致在多样驾驶场景中难以兼顾安全与速度。为此，我们提出使用强化学习（RL）代理作为高层行为选择器，在运行时动态调整低层解析式轨迹规划器的成本函数参数。在自主赛车仿真环境中，我们的方法实现了0%碰撞率，同时相比最先进的静态规划器将超车时间最多减少60%。新代理可动态切换激进与保守行为，实现传统静态配置无法达成的交互式操控。结果表明，将强化学习作为高层选择器，有效解决了自动驾驶赛车规划中安全与竞争力之间的固有权衡。该方法为更广泛自动驾驶应用中的自适应且可解释的运动规划提供了可行路径。
原文摘要 · Abstract (English)
Sampling-based trajectory planners are widely used for agile autonomous driving due to their ability to generate fast, smooth, and kinodynamically feasible trajectories. However, their behavior is often governed by a cost function with manually tuned, static weights, which forces a tactical compromise that is suboptimal across the wide range of scenarios encountered in a race. To address this shortcoming, we propose using a Reinforcement Learning (RL) agent as a high-level behavioral selector that dynamically switches the cost function parameters of an analytical, low-level trajectory planner during runtime. We show the effectiveness of our approach in simulation in an autonomous racing environment where our RL-based planner achieved 0% collision rate while reducing overtaking time by up to 60% compared to state-of-the-art static planners. Our new agent now dynamically switches between aggressive and conservative behaviors, enabling interactive maneuvers unattainable with static configurations. These results demonstrate that integrating reinforcement learning as a high-level selector resolves the inherent trade-off between safety and competitiveness in autonomous racing planners. The proposed methodology offers a pathway toward adaptive yet interpretable motion planning for broader autonomous driving applications.
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