arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2002.04794cs.RO

Computing the racing line using Bayesian optimization

University of Pennsylvania(宾夕法尼亚大学)

Achin Jain, Manfred Morari

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑