用混合方法提升驾驶行为分析的准确率与可解释性
Hybrid Approach for Driver Behavior Analysis with Machine Learning, Feature Optimization, and Explainable AI
- 结合特征优化与可解释AI，提升模型性能
- 随机森林准确率达95%，优化后仍保持94.2%精度
- 适合交通安全系统开发与模型可信性研究
驾驶行为分析对提升道路安全、减少激进或分心驾驶至关重要。以往研究多采用机器学习与深度学习，但普遍存在特征优化不足，影响性能与可解释性。本文提出一种混合方法，基于来自Kaggle的12,857行、18列数据集，经标签编码、随机过采样和标准化处理后，测试了13种机器学习算法。随机森林分类器达到95%准确率。引入XAI中的LIME技术，识别出对准确率影响最大的前10个正负向特征，并重新训练模型。随机森林准确率微降至94.2%，验证了在不损失性能的前提下可进一步提升模型效率。该混合模型在预测能力与可解释性之间实现良好平衡，具备实际应用价值。
原文摘要 · Abstract (English)
Progressive driver behavior analytics is crucial for improving road safety and mitigating the issues caused by aggressive or inattentive driving. Previous studies have employed machine learning and deep learning techniques, which often result in low feature optimization, thereby compromising both high performance and interpretability. To fill these voids, this paper proposes a hybrid approach to driver behavior analysis that uses a 12,857-row and 18-column data set taken from Kaggle. After applying preprocessing techniques such as label encoding, random oversampling, and standard scaling, 13 machine learning algorithms were tested. The Random Forest Classifier achieved an accuracy of 95%. After deploying the LIME technique in XAI, the top 10 features with the most significant positive and negative influence on accuracy were identified, and the same algorithms were retrained. The accuracy of the Random Forest Classifier decreased slightly to 94.2%, confirming that the efficiency of the model can be improved without sacrificing performance. This hybrid model can provide a return on investment in terms of the predictive power and explainability of the driver behavior process.
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