Anomaly Detection in Connected and Automated Vehicles using an Augmented State Formulation
| Autor: | Wang, Yiyang, Masoud, Neda, Khojandi, Anahita |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | 2020 Forum on Integrated and Sustainable Transportation Systems (FISTS), Delft, Netherlands, 2020, pp. 156-161 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/FISTS46898.2020.9264885 |
| Popis: | 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 $\chi^2$ 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 $\chi^2$-detector can achieve a high anomaly detection performance. Comment: Accepted to be Published in: 2020 Forum on Integrated and Sustainable Transportation Systems (FISTS), Delft, Netherlands, 2020, pp. 156-161. arXiv admin note: text overlap with arXiv:1911.01531 |
| Databáze: | arXiv |
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