Anomaly Detection in Connected and Automated Vehicles using an Augmented State Formulation

@article{Wang2020AnomalyDI,
  title={Anomaly Detection in Connected and Automated Vehicles using an Augmented State Formulation},
  author={Yiyang Wang and Neda Masoud and Anahita Khojandi},
  journal={2020 Forum on Integrated and Sustainable Transportation Systems (FISTS)},
  year={2020},
  pages={156-161}
}
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… Expand

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