Scholarlink

<p>Accurately estimating the road adhesion coefficient is essential for ensuring vehicle driving safety. This study proposes a method that integrates a seven-degree-of-freedom vehicle model and the magic formula tire model with the eXtreme Gradient Boosting algorithm, leveraging Shapley Additive exPlanations values to determine feature importance. Based on this, a road adhesion coefficient estimation model is constructed, incorporating a Bayesian neural network and a Support vector regression to enhance interpretability. To address potential issues such as the model predicting road adhesion coefficients beyond the range present in the training data, a data fusion strategy is designed to improve generalization. Simulation results demonstrate that the fusion estimator outperforms individual models in terms of robustness, real-time performance, and prediction accuracy, especially under varying vehicle speeds and road conditions. Compared to a single estimator, the data fusion model reduced the mean square error by an average of 16.01% and 18.89%, respectively, while the root mean square error was reduced by an average of 11.33% and 16.04%, respectively. This approach provides valuable theoretical support to advance the development of vehicle active safety systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Predicting road adhesion coefficient with a fusion strategy of SHAP dynamic parameters

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月13日

查看全文
  • Yi Gong,
  • Qiuyue Du,
  • Fei Wang,
  • Limei Zhang

摘要

Accurately estimating the road adhesion coefficient is essential for ensuring vehicle driving safety. This study proposes a method that integrates a seven-degree-of-freedom vehicle model and the magic formula tire model with the eXtreme Gradient Boosting algorithm, leveraging Shapley Additive exPlanations values to determine feature importance. Based on this, a road adhesion coefficient estimation model is constructed, incorporating a Bayesian neural network and a Support vector regression to enhance interpretability. To address potential issues such as the model predicting road adhesion coefficients beyond the range present in the training data, a data fusion strategy is designed to improve generalization. Simulation results demonstrate that the fusion estimator outperforms individual models in terms of robustness, real-time performance, and prediction accuracy, especially under varying vehicle speeds and road conditions. Compared to a single estimator, the data fusion model reduced the mean square error by an average of 16.01% and 18.89%, respectively, while the root mean square error was reduced by an average of 11.33% and 16.04%, respectively. This approach provides valuable theoretical support to advance the development of vehicle active safety systems.