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

2021/05/25 by Tim Brüdigam, Alexandre Capone, Brüdigam, Tim +7 · 2 citations
Computer Science · Engineering · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Real-time simulation and control systems #Robotic Path Planning Algorithms #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2105.12236

openalex publication_date 2021/05/25 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

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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• Accelerating Real-World Overtaking in F1TENTH Racing Employing Reinforcement Learning Methods
• IteraOptiRacing: A Unified Planning-Control Framework for Real-time Autonomous Racing for Iterative Optimal Performance

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