Environmental perception system is the premise of the safety and
stability of the autonomous vehicle system. However, studies have shown
that the on-board sensors included in the perception system are
extremely vulnerable to external attacks and interference, leading to
incorrect driving strategies and bringing great security threats. Aiming
at the problem, this paper divides the vehicle-mounted sensors into a
positioning group and an identification group according to their role in
the perception system. Then, based on the information correlation
between sensors in the same group and the information correlation of a
single sensor on adjacent time series, the distance metric model between
sensors in a group and the distance metric model for each sensor of this
group on time series is established. And the normal distance intervals
corresponding to the confidence interval are calculated respectively.
According to the distance metric model between sensors, we can detect
anomalies in the perception system in real-time. Further, according to
the distance metric model for each sensor on adjacent time series, we
can identify anomaly sensors. Our experimental results quantitatively
show that the method achieves real-time anomaly recognition, and
demonstrate the effectiveness and robustness of the method on the
open-source KITTI dataset.