ADS-Lead: Lifelong Anomaly Detection in Autonomous Driving Systems

Abstract

Autonomous Vehicles (AVs) are closely connected in the Cooperative Intelligent Transportation System (C-ITS). They are equipped with various sensors and controlled by Autonomous Driving Systems (ADSs) to provide high-level autonomy. The vehicles exchange different types of real-time data with each other, which can help reduce traffic accidents and congestion, and improve the efficiency of transportation systems. However, when interacting with the environment, AVs suffer from a broad attack surface, and the sensory data are susceptible to anomalies caused by faults, sensor malfunctions, or attacks, which may jeopardize traffic safety and result in serious accidents. In this paper, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ADS-Lead</monospace> , an efficient collaborative anomaly detection methodology to protect the lane-following mechanism of ADSs. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ADS-Lead</monospace> is equipped with a novel transformer-based one-class classification model to identify time series anomalies (GPS spoofing threat) and adversarial image examples (traffic sign and lane recognition attacks). Besides, AVs inside the C-ITS form a cognitive network, enabling us to apply the federated learning technology to our anomaly detection method, where the vehicles in the C-ITS jointly update the detection model with higher model generalization and data privacy. Experiments on Baidu Apollo and two public data sets (GTSRB and Tumsimple) indicate that our method can not only detect sensor anomalies effectively and efficiently but also outperform state-of-the-art anomaly detection methods.