用可解释机器学习识别驾驶风格并给出安全建议
XAI-Driven Machine Learning System for Driving Style Recognition and Personalized Recommendations
- 结合随机森林与梯度提升，兼顾精度与可解释性
- 在三分类任务中准确率达0.92，媲美深度学习模型
- 通过SHAP分析提供个性化驾驶改进建议，适合智能交通应用
人工智能在汽车领域广泛应用，如驾驶风格分类，以提升道路安全、效率并个性化用户体验。尽管深度学习模型（如LSTM）表现优异，但其黑箱特性限制了可解释性与信任度。本文提出一种基于机器学习的方法，在保持高精度的同时提升可解释性。构建了高质量数据集CARLA-Drive，采用随机森林（RF）、梯度提升（XGBoost）和支持向量机（SVM）等高效轻量的可解释模型。结合SHAP解释技术，为用户提供个性化的安全驾驶建议。在三类驾驶风格分类任务中，RF与XGBoost均达到0.92的准确率，性能媲美深度学习模型，兼具透明性与实际部署可行性，适用于智能交通系统。
原文摘要 · Abstract (English)
Artificial intelligence (AI) is increasingly used in the automotive industry for applications such as driving style classification, which aims to improve road safety, efficiency, and personalize user experiences. While deep learning (DL) models, such as Long Short-Term Memory (LSTM) networks, excel at this task, their black-box nature limits interpretability and trust. This paper proposes a machine learning (ML)-based method that balances high accuracy with interpretability. We introduce a high-quality dataset, CARLA-Drive, and leverage ML techniques like Random Forest (RF), Gradient Boosting (XGBoost), and Support Vector Machine (SVM), which are efficient, lightweight, and interpretable. In addition, we apply the SHAP (Shapley Additive Explanations) explainability technique to provide personalized recommendations for safer driving. Achieving an accuracy of 0.92 on a three-class classification task with both RF and XGBoost classifiers, our approach matches DL models in performance while offering transparency and practicality for real-world deployment in intelligent transportation systems.
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