让赛车规划主动学习控制误差，跑得更快更安全。
Control-Informed Constraint Adaptation in Minimum-Time Trajectory Planning for Autonomous Racing
- 用运行时跟踪偏差动态调整赛道约束
- 实测单圈快1.8秒，计算耗时仍保持25毫秒
- 适合追求极限性能的自动驾驶赛车系统
自动驾驶赛车在车辆动力学极限下运行，微小的控制误差会引发安全问题并导致性能损失。传统轨迹规划假设完美跟踪，对执行误差视而不见，因而只能采用保守的空间裕度，浪费可用赛道空间。为此，我们提出一种控制感知的在线轨迹规划框架，通过实时测量系统性跟踪偏差，动态调整空间赛道约束，逐步扩展自由规划区域。该方法在高保真闭环仿真环境中验证，可在保持时间最优的同时补偿累积执行误差。结果表明，该方法使单圈时间减少1.8秒，计算耗时中位数维持在25毫秒，未增加计算负担。研究显示，将控制误差反馈至规划层，可突破模块化架构性能瓶颈，使自动驾驶车辆系统性利用赛道极限。
原文摘要 · Abstract (English)
Autonomous racecars operate at the limits of vehicle dynamics, where small control errors translate into safety-critical behavior and lost performance. Trajectory planners assume perfect tracking and remain blind to execution errors. To guarantee safety, trajectory planners therefore restrict themselves to conservative spatial margins, leaving usable track space untapped. To overcome these issues, we introduce a control-informed online trajectory planning framework that learns from its own execution errors. By measuring systematic tracking deviations during runtime, we dynamically adapt spatial track constraints and iteratively expand the free-space planning area. The planner remains time-optimal while compensating for accumulated execution errors. This method was analyzed in a high-fidelity closed-loop simulation environment with autonomous racecars. The results demonstrate that our approach reduces lap time by 1.8\,s without increasing computational burden, maintaining a median runtime of 25 ms. Our finding indicates that feeding control-induced deviations back into the planning layer unlocks performance previously inaccessible to modular architectures and enables autonomous vehicles to exploit track limits systematically.
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