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

• Published in: arXiv.org
• Main Authors: Wang, Yiyang, Masoud, Neda, Khojandi, Anahita
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 25.02.2021
• Subjects: Adaptive filters; Anomalies; Automation; Car following; Communications traffic; Extended Kalman filter; Sensors; Statistics - Machine Learning; Support vector machines; Time lag; Traffic delay
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1911.01531

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.