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- W3106860295 abstract "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." @default.
- W3106860295 created "2020-12-07" @default.
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- W3106860295 date "2020-11-03" @default.
- W3106860295 modified "2023-09-24" @default.
- W3106860295 title "Anomaly Detection in Connected and Automated Vehicles using an Augmented State Formulation" @default.
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- W3106860295 doi "https://doi.org/10.1109/fists46898.2020.9264885" @default.
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