Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors

In this paper we propose a novel observer-based method to improve the safety and security of connected and automated vehicle (CAV) transportation.The proposed method combines model-based signal filtering and anomaly detection methods.Specifically, we use adaptive extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following motion model.Under the assumption of a carfollowing model, the subject vehicle utilizes its leading vehicle's information to detect sensor anomalies by employing previouslytrained One Class Support Vector Machine (OCSVM) models.This approach allows the AEKF to estimate the state of a vehicle not only based on the vehicle's location and speed, but also by taking into account the state of the surrounding traffic.A communication time delay factor is considered in the carfollowing model to make it more suitable for real-world applications.Our experiments show that compared with the AEKF with a traditional χ 2 -detector, our proposed method achieves a better anomaly detection performance.We also demonstrate that a larger time delay factor has a negative impact on the overall detection performance.