Data-driven anomaly detection in autonomous platoon

Abstract

Technology brings autonomous vehicles into a reality where vehicles cruise themselves without human input. Vehicular platoon, on the other hand, is a group of autonomous vehicles that are organized into close proximity through wireless communication. In an autonomous platoon, vehicles cooperatively send data to each other to adjust their speed and distance to the leader, the first vehicle in the platoon. However, this cooperative data exchange can lead to security risks. A misbehaving platoon member could alter the data packets which may cause platoon instability. Therefore, identifying the modified packets has become an important requirement. In this paper, we investigate data-driven anomaly detection mechanisms for the autonomous platoon. We propose a novel statistical learning based technique to detect the modified packets and misbehaving vehicles. We demonstrate that the distance change to the leader would be sufficient to detect anomalies and misbehavior.