A Trust-Aware POMDP Framework for Thwarting Data Falsification Attacks in Cooperative Driving

We address the problem of data falsification attacks (DFAs) in cooperative autonomous vehicles. In DFAs, compromised or malicious vehicles broadcast false navigation information (acceleration, velocity, position, steering, motion intent) to influence the control algorithm of neighboring vehicles and cause collisions, traffic jams, and passenger discomfort. To maintain safe and efficient driving under DFAs, we propose a trust-aware par-tially observable Markov decision process (POMDP) framework that accounts for the dynamic uncertainty caused by Byzantine behaviors. Compared to traditional POMDP formulations, our framework integrates data fusion between sensing data and V2V messages across different data sampling rates, thereby addressing the challenge of achieving robust real-time control despite delayed data verification. Moreover, the incorporation of trust allows for quickly shifting real-time control between a sensors-only mode (safer but less efficient) to a sensor plus V2V messages mode (less safe but more efficient), depending on the trust level. Extensive simulations with real traffic data demonstrate that our method accurately maintains the vehicle's true state, prevents catastrophic vehicle collisions while ensuring passenger comfort and efficiency.