Autonomous Driving Policy

Autonomous driving policy research centers on developing algorithms that enable safe and efficient vehicle navigation, primarily focusing on optimizing both driving behavior and communication network selection. Current efforts leverage reinforcement learning, often employing deep Q-networks or variations thereof, to learn optimal policies from simulated and real-world driving data, incorporating multi-objective frameworks to balance competing goals like safety, traffic flow, and communication reliability. This research is crucial for advancing the safety and reliability of autonomous vehicles, impacting both the development of robust control systems and the design of supporting communication infrastructures.