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

arXiv: https://arxiv.org/abs/2105.12236 · HTML: https://mlsanity.com/paper/2105.12236/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2021.

## Built on by (most cited first, 5 of 5)

- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Predictive Control for Autonomous Driving with Uncertain, Multi-modal Predictions](https://mlsanity.com/paper/2310.20561/) (2023)
- [Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes](https://mlsanity.com/paper/2306.03405/) (2023)
- [Identifying Reaction-Aware Driving Styles of Stochastic Model Predictive Controlled Vehicles by Inverse Reinforcement Learning](https://mlsanity.com/paper/2308.12069/) (2023)
- [Accelerating Real-World Overtaking in F1TENTH Racing Employing Reinforcement Learning Methods](https://mlsanity.com/paper/2510.26040/) (2025)

## Abstract (arXiv)

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.