Secure Cooperative Localization for Connected Automated Vehicles Based on Consensus

In this article, we present secure cooperative localization for connected automated vehicles (CAVs) based on consensus estimation through leveraging shared but possibly attacked sensory information from multiple adjacent vehicles. First, the communication topology between the CAVs, node kinematic model, and node measurement model for each vehicle are introduced. Then, a consensus Kalman information filter (CKIF) is applied to fuse the shared information from connected vehicles. Since the sensory information might be attacked, an attack detection algorithm based on the generalized likelihood ratio test (GLRT) is adopted. A delay-prediction framework is proposed to maintain the accuracy and real-time performance of the detection algorithm. Next, a rule-based attack isolation method is used to defend the attack. Finally, the proposed secure cooperative localization algorithm is validated in extensive numerical simulation experiments. The results confirm that leveraging information from multiple vehicles in a cooperative manner leads to better accuracy and resilience for vehicle localization under attacks.