论文标题
Bayesrace：学习使用先前的经验自主比赛
BayesRace: Learning to race autonomously using prior experience
论文作者
论文摘要
自动赛车需要感知，估计，计划和控制模块，这些模块在以车辆的处理能力的极限为准时，它们不同步。设计这些软件组件时遇到的一个基本挑战在于以高精度预测车辆的未来状态（例如位置，方向和速度）。根本原因是识别捕获侧向轮胎滑动影响的车辆模型参数的困难。我们提出了一个基于模型的计划和控制框架，用于自主赛车，可大大减少系统识别和控制设计所需的努力。我们的方法通过从板载传感器测量值中学习来减轻基于模拟的控制器设计引起的差距。这项工作的主要重点是经验，因此，我们通过对验证的1:43和1:10规模自主赛车模拟的实验来证明我们的贡献。
Autonomous race cars require perception, estimation, planning, and control modules which work together asynchronously while driving at the limit of a vehicle's handling capability. A fundamental challenge encountered in designing these software components lies in predicting the vehicle's future state (e.g. position, orientation, and speed) with high accuracy. The root cause is the difficulty in identifying vehicle model parameters that capture the effects of lateral tire slip. We present a model-based planning and control framework for autonomous racing that significantly reduces the effort required in system identification and control design. Our approach alleviates the gap induced by simulation-based controller design by learning from on-board sensor measurements. A major focus of this work is empirical, thus, we demonstrate our contributions by experiments on validated 1:43 and 1:10 scale autonomous racing simulations.
