融合多种可解释AI方法，提升自动驾驶异常检测的准确与可信度。
XAI-based Feature Ensemble for Enhanced Anomaly Detection in Autonomous Driving Systems
- 用SHAP、LIME等XAI方法融合多模型特征，增强检测能力。
- 在VeReMi和Sensor数据集上，检测准确率显著提升且结果更透明。
- 适合关注自动驾驶安全与模型可信性的研究人员和工程师。
自动驾驶技术快速发展，但其异常检测模型常因黑箱特性难以理解与信任。本文提出一种基于可解释AI（XAI）的特征集成框架，整合SHAP、LIME、DALEX等多种XAI方法，与六类主流模型（决策树、随机森林、深度神经网络、K近邻、支持向量机、AdaBoost）结合，筛选并融合关键特征。所生成的特征集通过独立分类器（CatBoost、逻辑回归、LightGBM）评估，确保性能无偏。在两个主流自动驾驶数据集（VeReMi 和 Sensor）上验证，该方法显著提升了异常检测的准确性、鲁棒性与模型透明度，推动更安全可信的自动驾驶系统发展。
原文摘要 · Abstract (English)
The rapid advancement of autonomous vehicle (AV) technology has introduced significant challenges in ensuring transportation security and reliability. Traditional AI models for anomaly detection in AVs are often opaque, posing difficulties in understanding and trusting their decision making processes. This paper proposes a novel feature ensemble framework that integrates multiple Explainable AI (XAI) methods: SHAP, LIME, and DALEX with various AI models to enhance both anomaly detection and interpretability. By fusing top features identified by these XAI methods across six diverse AI models (Decision Trees, Random Forests, Deep Neural Networks, K Nearest Neighbors, Support Vector Machines, and AdaBoost), the framework creates a robust and comprehensive set of features critical for detecting anomalies. These feature sets, produced by our feature ensemble framework, are evaluated using independent classifiers (CatBoost, Logistic Regression, and LightGBM) to ensure unbiased performance. We evaluated our feature ensemble approach on two popular autonomous driving datasets (VeReMi and Sensor) datasets. Our feature ensemble technique demonstrates improved accuracy, robustness, and transparency of AI models, contributing to safer and more trustworthy autonomous driving systems.
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