Real-Time Sensor Anomaly Detection and Identification in Automated Vehicles

Abstract

Connected and automated vehicles (CAVs) are expected to revolutionize the transportation industry, mainly through allowing for a real-time and seamless exchange of information between vehicles and roadside infrastructure. Although connectivity and automation are projected to bring about a vast number of benefits, they can give rise to new challenges in terms of safety, security, and privacy. To navigate roadways, CAVs need to heavily rely on their sensor readings and the information received from other vehicles and roadside units. Hence, anomalous sensor readings caused by either malicious cyber attacks or faulty vehicle sensors can result in disruptive consequences and possibly lead to fatal crashes. As a result, before the mass implementation of CAVs, it is important to develop methodologies that can detect anomalies and identify their sources seamlessly and in real time. In this paper, we develop an anomaly detection approach through combining a deep learning method, namely convolutional neural network (CNN), with a well-established anomaly detection method, and Kalman filtering with a $\chi ^{2}$ -detector, to detect and identify anomalous behavior in CAVs. Our numerical experiments demonstrate that the developed approach can detect anomalies and identify their sources with high accuracy, sensitivity, and F1 score. In addition, this developed approach outperforms the anomaly detection and identification capabilities of both CNNs and Kalman filtering with a $\chi ^{2}$ -detector method alone. It is envisioned that this research will contribute to the development of safer and more resilient CAV systems that implement a holistic view toward intelligent transportation system (ITS) concepts.