用贝叶斯优化同时估算轮胎参数及其不确定性，提升实测数据建模可靠性。
Bayesian Optimization-based Tire Parameter and Uncertainty Estimation for Real-World Data
- 采用贝叶斯优化与变分推断联合估计参数及不确定性
- 实测数据中低滑移率导致曲率与形状参数识别不准确
- 可评估数据质量并指导轮胎建模改进，适合自动驾驶研发人员
本文提出一种基于贝叶斯优化的方法，用于估计轮胎参数及其不确定性。现有研究多关注参数估计，却缺乏对识别质量及所需滑移率的评估。为此，我们采用随机变分推断方法，同时估计参数及其不确定性，并在理论与真实数据场景下与先进Nelder-Mead算法对比。理论分析考察不同滑移率下的参数拟合效果，结果与Pacejka魔术公式模型的敏感性分析对比。实际应用基于阿布扎比自动驾驶赛车联盟采集的数据，揭示在低滑移率下曲率与形状参数的不确定性较高。该方法可评估真实条件下参数的均值与不确定性，为轮胎建模提供依据，并建议在高滑移率数据未获取前使用标准化参数。
原文摘要 · Abstract (English)
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.
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