# Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes

arXiv: https://arxiv.org/abs/2306.03405 · HTML: https://mlsanity.com/paper/2306.03405/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [F1/10: An Open-Source Autonomous Cyber-Physical Platform](https://mlsanity.com/paper/1901.08567/) (2019)
- [Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Cars](https://mlsanity.com/paper/1711.06586/) (2017)
- [Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits](https://mlsanity.com/paper/2202.03807/) (2022)
- [BayesRace: Learning to race autonomously using prior experience](https://mlsanity.com/paper/2005.04755/) (2020)
- [Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing](https://mlsanity.com/paper/2105.12236/) (2021)
- [A Gaussian Process Model for Opponent Prediction in Autonomous Racing](https://mlsanity.com/paper/2204.12533/) (2022)

## Built on by (most cited first, 1 of 1)

- [Analyzing the Impact of Simulation Fidelity on the Evaluation of Autonomous Driving Motion Control](https://mlsanity.com/paper/2602.07984/) (2026)

## Abstract (arXiv)

Autonomous racing is increasingly becoming a proving ground for autonomous vehicle technology at the limits of its current capabilities. The most prominent examples include the F1Tenth racing series, Formula Student Driverless (FSD), Roborace, and the Indy Autonomous Challenge (IAC). Especially necessary, in high speed autonomous racing, is the knowledge of accurate racecar vehicle dynamics. The choice of the vehicle dynamics model has to be made by balancing the increasing computational demands in contrast to improved accuracy of more complex models. Recent studies have explored learning-based methods, such as Gaussian Process (GP) regression for approximating the vehicle dynamics model. However, these efforts focus on higher level constructs such as motion planning, or predictive control and lack both in realism and rigor of the GP modeling process, which is often over-simplified. This paper presents the most detailed analysis of the applicability of GP models for approximating vehicle dynamics for autonomous racing. In particular we construct dynamic, and extended kinematic models for the popular F1TENTH racing platform. We investigate the effect of kernel choices, sample sizes, racetrack layout, racing lines, and velocity profiles on the efficacy and generalizability of the learned dynamics. We conduct 400+ simulations on real F1 track layouts to provide comprehensive recommendations to the research community for training accurate GP regression for single-track vehicle dynamics of a racecar.