  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.
