Dynamic Model Based Malicious Collaborator Detection in Cooperative Tracking

Abstract

The mobility status of vehicles play a crucial role in most tasks of Autonomous Vehicles (AVs) and Intelligent Transportation System (ITS). To operate securely, a precise, stable and robust mobility tracking system is essential. Compared with self-tracking that relies only on mobility observations from on-board sensors (e.g. Global Positioning System (GPS), Inertial Measurement Unit (IMU) and camera), cooperative tracking increases the precision and reliability of mobility data greatly by integrating observations from road side units and nearby vehicles through V2X communications. Nevertheless, cooperative tracking can be quite vulnerable if there are malicious collaborators sending bogus observations in the network. In this paper, we present a dynamic sequential detection algorithm, dynamic model based mean state detection (DMMSD), to exclude bogus mobility data. Simulations validate the effectiveness and robustness of the proposed algorithm as compared with existing approaches.