Enhancing Misbehavior Detection in 5G Vehicle-to-Vehicle Communications

Next-generation advanced driver-assistance systems (ADAS) and cooperative adaptive cruise control (CACC) for advanced/autonomous driving are expected to increasingly use wireless connectivity such as V2V and V2I to improve the coverage, particularly in the locations where a vehicle's camera or radar is ineffective. However, using shared sensing data raises grave concerns about the truthfulness of information reported by unreliable stakeholders. For example, a transmitting vehicle may deliberately disseminate false locations to the surrounding receivers. Trusting the data, the automatic control systems in such connected receivers can be trapped to change to a wrong lane or accelerate unexpectedly, and then potentially lead to a crash. This work introduces a novel approach to support a host vehicle in verifying the motion behavior of a target vehicle and then the truthfulness of sharing data in cooperative vehicular communications. Initially, at the host vehicle, the detection system recreates the motion behavior of the target vehicle by extracting the positioning information from the V2V received messages. Furthermore, the next states of that vehicle are predicted based on the unscented Kalman filter. Unlike prior studies, the checkpoints of the predicted trajectory in the update stage are periodically corrected with a new reliable measurement source, namely 5 G V2V multi-array beamforming localization. If there is any inconsistency between the estimated position and the corresponding reported one from V2V, the target vehicle will be classified as an abnormal one. The simulation results demonstrate that our method can achieve accuracy over 0.97 in detecting abnormal reports, including those from collusion and Sybil attacks.