Computing the racing line using Bayesian optimization

Achin Jain, Manfred Morari
2/12/2020
cs.RO

Official code: jainachin/bayesrace (83 stars, MIT, last commit 2024-06-17).

Abstract

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.

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Code Implementations(1)

jainachin/bayesraceOfficial100%
MIT

Learning based control for autonomous racing

8327PythonFeb 11, 20202 years agoMIT

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Cite this paper

@article{jain2020computing,
  title  = {Computing the racing line using Bayesian optimization},
  author = {Achin Jain and Manfred Morari},
  year   = {2020},
  eprint = {2002.04794},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2002.04794}
}

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