Deep Real-Time Anomaly Detection for Connected Autonomous Vehicles
Rachid Oucheikh, Mouhsene Fri, Fayçal Fedouaki, Mustapha Hain
Abstract
Connected and autonomous vehicles (CAV) are expected to change the landscape of the automotive market. They are autonomous decision-making systems that process streams of observations coming from different external and on-board sensors. CAV like any other cyber-physical objects are prone to signal interference, hardware deterioration, software errors, power instability, and cyber-attacks. To avoid these anomalies which can be fatal, it is mandatory to design a robust real-time technique to detect them and identify their sources. In this paper, we propose a deep learning approach which consists of hierarchic models to firstly extract the signal features using an LSTM auto-encoder, then perform an accurate classification of each signal sequence in real-time. In addition, we investigated the impact of the model parameter tuning on the anomaly detection and the advantage of channel boosting through three scenarios. The model achieves an accuracy of 95.5% and precision of 94.2%.