Anomaly Detection in Connected and Automated Vehicles using an Augmented State Formulation
This addresses sensor reliability issues for CAV safety, but it is incremental as it builds on existing Kalman filter and fault detection techniques.
The paper tackles anomaly detection in connected and automated vehicles by proposing an observer-based method using an augmented extended Kalman filter with a χ² detector, achieving high performance in experiments on real-world data.
In this paper we propose a novel observer-based method for anomaly detection in connected and automated vehicles (CAVs). The proposed method utilizes an augmented extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following motion model with time delay, where the leading vehicle's trajectory is used by the subject vehicle to detect sensor anomalies. We use the classic $χ^2$ fault detector in conjunction with the proposed AEKF for anomaly detection. To make the proposed model more suitable for real-world applications, we consider a stochastic communication time delay in the car-following model. Our experiments conducted on real-world connected vehicle data indicate that the AEKF with $χ^2$-detector can achieve a high anomaly detection performance.