论文标题
使用贝叶斯优化计算赛车线
Computing the racing line using Bayesian optimization
论文作者
论文摘要
一项良好的赛车策略，尤其是赛车线是在一级方程式赛车，MotoGP和其他形式的赛车中赢得比赛的决定性的。赛车线定义了沿轨道以及沿路径的最佳速度轮廓的路径。目的是通过在摩擦和处理能力的范围内驾驶车辆来最大程度地减少膝上时间。解决方案自然取决于轨道和车辆动力学的几何形状。我们介绍了一种使用贝叶斯优化来计算赛车线的新方法。与基于动态编程和随机搜索的其他方法相比，我们的方法完全由数据驱动，计算效率更高。该方法在自主赛车中特别相关，在该赛车中，团队可以快速计算新轨道的赛车线，然后在运动计划者的设计和控制器的设计中利用此信息来优化实时性能。
A good racing strategy and in particular the racing line is decisive to winning races in Formula 1, MotoGP, and other forms of motor racing. The racing line defines the path followed around a track as well as the optimal speed profile along the path. The objective is to minimize lap time by driving the vehicle at the limits of friction and handling capability. The solution naturally depends upon the geometry of the track and vehicle dynamics. We introduce a novel method to compute the racing line using Bayesian optimization. Our approach is fully data-driven and computationally more efficient compared to other methods based on dynamic programming and random search. The approach is specifically relevant in autonomous racing where teams can quickly compute the racing line for a new track and then exploit this information in the design of a motion planner and a controller to optimize real-time performance.
