# XAI-based Feature Ensemble for Enhanced Anomaly Detection in Autonomous Driving Systems

arXiv: https://arxiv.org/abs/2410.15405 · HTML: https://mlsanity.com/paper/2410.15405/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 20 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [A2D2: Audi Autonomous Driving Dataset](https://mlsanity.com/paper/2004.06320/) (2020)
- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs](https://mlsanity.com/paper/1804.06701/) (2018)
- [Strategies to exploit XAI to improve classification systems](https://mlsanity.com/paper/2306.05801/) (2023)
- [Toward the application of XAI methods in EEG-based systems](https://mlsanity.com/paper/2210.06554/) (2022)
- [A Systematic Survey of Attack Detection and Prevention in Connected and Autonomous Vehicles](https://mlsanity.com/paper/2203.14965/) (2022)

## Abstract (arXiv)

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.