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arXiv · 2105.12236

Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing

Tim Brüdigam·Alexandre Capone·Sandra Hirche·Dirk Wollherr·Marion Leibold
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Abstract

A fundamental aspect of racing is overtaking other race cars. Whereas previous research on autonomous racing has majorly focused on lap-time optimization, here, we propose a method to plan overtaking maneuvers in autonomous racing. A Gaussian process is used to learn the behavior of the leading vehicle. Based on the outputs of the Gaussian process, a stochastic Model Predictive Control algorithm plans optimistic trajectories, such that the controlled autonomous race car is able to overtake the leading vehicle. The proposed method is tested in a simple simulation scenario.

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Tim Brüdigam, Alexandre Capone, Sandra Hirche, Dirk Wollherr, Marion Leibold. 2021-05-25. Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing. https://arxiv.org/abs/2105.12236

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