Time-Driven and Privacy-Preserving Navigation Model for Vehicle-to-Vehicle Communication Systems

Effective time-driven navigation is an operative way to alleviate traffic congestion, which is also a challenging problem in the Internet of Vehicles context. Most existing centralized navigation systems often cannot react promptly to real-time local traffic situations, while most existing distributed navigation systems are vulnerable to privacy attacks. To overcome these drawbacks, we propose a learning model that provides a provable guarantee of vehicles' privacy while still enabling efficient navigation under real-time traffic conditions. The proposed model adopts a novel multi-agent system with customized differentially private mechanisms. To verify the effectiveness and stability of our approach, we implement the proposed method on CARLA, which is an autonomous driving simulator. In four experimental tasks with varying parameters, we demonstrate fully that our proposed method outperforms other benchmarks.