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Bayesian Optimization-based Tire Parameter and Uncertainty Estimation for Real-World Data

arXiv · · Notable

RL Robotics Research Optimization Autonomous vehicles

Summary

This paper introduces a Bayesian optimization method for estimating tire parameters and their uncertainty, addressing a gap in existing literature. The methodology uses Stochastic Variational Inference to estimate parameters and uncertainties, and it is validated against a Nelder-Mead algorithm. The approach is applied to real-world data from the Abu Dhabi Autonomous Racing League, revealing uncertainties in identifying curvature and shape parameters due to insufficient excitation. Why it matters: The research provides a practical tool for assessing tire model parameters in real-world conditions, with implications for autonomous racing and vehicle dynamics modeling in the GCC region.

Keywords

Bayesian optimization · tire parameters · uncertainty estimation · autonomous racing · Abu Dhabi Autonomous Racing League

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