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

TL;DR

This paper introduces a real-time, observer-based anomaly detection method for connected automated vehicle sensors that combines adaptive Kalman filtering with machine learning, improving detection accuracy in realistic traffic scenarios.

Contribution

The paper presents a novel integration of adaptive extended Kalman filter and one-class SVM for sensor anomaly detection in CAVs, considering communication delays and traffic context.

Findings

The proposed method outperforms traditional chi-squared detectors in anomaly detection accuracy.

Increased communication delay negatively affects detection performance.

Incorporating traffic context improves sensor anomaly detection in CAVs.

Abstract

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 car-following model, the subject vehicle utilizes its leading vehicle's information to detect sensor anomalies by employing previously-trained 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 car-following model to make it more suitable for real-world applications. Our experiments show that compared with the AEKF with a traditional $\chi^2$-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.