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

TL;DR

Addresses the challenge of estimating tire parameters from real-world data with quantified uncertainty and understanding the required slip-excitation for reliable identifiability. The authors employ Stochastic Variational Inference to jointly infer the Pacejka Simple model parameters $B$, $C$, $D$, and $E$ and their uncertainties, and they benchmark against Nelder-Mead in both simulated and real driving data from the Abu Dhabi Autonomous Racing League. Key contributions include the introduction of an open-source parametrization tool, a sensitivity analysis using Sobol indices, and practical guidance on excitation requirements and parameter fixing when data are insufficient. The work advances robust tire modeling for real-world vehicle dynamics by providing actionable insights into data needs and limitations and outlining avenues for extension to more sophisticated tire models.

Abstract

This work presents a methodology to estimate tire parameters and their uncertainty using a Bayesian optimization approach. The literature mainly considers the estimation of tire parameters but lacks an evaluation of the parameter identification quality and the required slip ratios for an adequate model fit. Therefore, we examine the use of Stochastical Variational Inference as a methodology to estimate both - the parameters and their uncertainties. We evaluate the method compared to a state-of-the-art Nelder-Mead algorithm for theoretical and real-world application. The theoretical study considers parameter fitting at different slip ratios to evaluate the required excitation for an adequate fitting of each parameter. The results are compared to a sensitivity analysis for a Pacejka Magic Formula tire model. We show the application of the algorithm on real-world data acquired during the Abu Dhabi Autonomous Racing League and highlight the uncertainties in identifying the curvature and shape parameters due to insufficient excitation. The gathered insights can help assess the acquired data's limitations and instead utilize standardized parameters until higher slip ratios are captured. We show that our proposed method can be used to assess the mean values and the uncertainties of tire model parameters in real-world conditions and derive actions for the tire modeling based on our simulative study.

Figures (6)

• Figure 1: Resulting tire curve and parameter uncertainties for the front axle of a Dallara EAV24 race car.
• Figure 2: Tire model fit for different excitation levels.
• Figure 3: Parameter fitting and uncertainty at different excitation levels. The black dotted line visualizes the reference parameters. The light blue area visualizes the doubled standard deviation.
• Figure 4: Sobol Index over slip ratio. The black dotted line shows the underlying tire force curve.
• Figure 5: Lateral tire curves of the Dallara EAV24 vehicle on the Yas Marina Circuit.