Ensemble Learning Framework for Anomaly Detection in Autonomous Driving Systems

Abstract

The inherent limitations of individual AI models underscore the need for robust anomaly detection techniques for securing autonomous driving systems. To address these limitations, we propose a comprehensive ensemble learning framework specifically designed for anomaly detection in autonomous driving systems. We comprehensively assess the effectiveness of ensemble learning models for detecting anomalies in autonomous vehicle datasets, focusing primarily on the VeReMi and Sensor datasets. Ensemble techniques are rigorously evaluated against individual models on binary and multiclass classification tasks. The analysis reveals that ensemble models consistently outperform individual models in terms of accuracy, precision, recall, false positive rates, and F1-score. On the VeReMi dataset, ensembles achieve high performance for binary classification, with a maximum accuracy of 0.80 and F1-score of 0.86, surpassing single models. For the Sensor dataset, ensemble models like CatBoost exhibit perfect accuracy, precision, recall, and F1-score, exceeding single models by 11% in accuracy. In VeReMi multiclass classification, Stacking and Blending gave a 5% increase in accuracy compared to single models. Moreover, XGBoost and CatBoost demonstrate perfect recall. Our proposed method enhanced performance despite the increased runtime required by ensemble models. In evaluating false positive rates, ensemble learning demonstrated significant gains, reducing false positives and thereby enhancing overall system reliability.