A data trust framework for VANETs enabling false data detection and secure vehicle tracking

Future automated vehicles will rely on V2V communication to exchange information about their motion states and take corresponding control actions, to enhance road safety and efficiency. Evaluating the trustworthiness of such data in a VANET is critical as malicious vehicles may inject false data which will undermine the benefits of V2V communication and lead to severe consequences, such as collisions. Existing solutions are inadequate since they assume an honest majority of vehicles. In this work, we propose a novel data trust framework, which determines the truthfulness of each received message on the fly, and is able to detect false data and securely track vehicles even when they report false information. The basic idea is to verify the implied effect of vehicle's reported data using secure sensing mechanisms from the wireless physical layer, which is wrapped within a dynamic vehicle tracking system using extended Kalman filter. Our framework does require at least one honest neighboring vehicle, and simulation results show that it is effective in most highway traffic scenarios.