Skip to content
GCC AI Research

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

Read original article →

Get the weekly digest

Top AI stories from the GCC region, every week.

Related

Minimalistic Autonomous Stack for High-Speed Time-Trial Racing

arXiv ·

This paper introduces a minimalistic autonomous racing stack designed for high-speed time-trial racing, emphasizing rapid deployment and efficient system integration with minimal on-track testing. Validated on real speedways, the stack achieved a top speed of 206 km/h within just 11 hours of practice, covering 325 km. The system performance analysis includes tracking accuracy, vehicle dynamics, and safety considerations. Why it matters: This research offers insights for teams aiming to quickly develop and deploy autonomous racing stacks with limited track access, potentially accelerating innovation in autonomous vehicle technology within the A2RL and similar racing initiatives.