Employing Reinforcement Learning in Autonomous Vehicle-to-Vehicle Communication Systems

Autonomous Vehicle-to-Vehicle (V2V) communication systems are critical for enabling safe, efficient, and coordinated transportation in intelligent traffic networks. This study explores the application of Reinforcement Learning (RL) to optimize V2V communication by dynamically adapting transmission strategies based on real-time network conditions. The proposed RL-based framework leverages multi-agent reinforcement learning to enhance data exchange efficiency, reduce communication latency, and improve system resilience against network congestion and failures. Experimental evaluations conducted in a simulated V2V environment demonstrated a 30% reduction in communication latency and a 25% improvement in data delivery reliability compared to traditional rule-based systems. Additionally, the RL framework achieved a 20% enhancement in overall system throughput, enabling smoother coordination among autonomous vehicles in high-density traffic scenarios. These results highlight the potential of RL in addressing the challenges of V2V communication, paving the way for more adaptive and intelligent vehicular networks. By dynamically optimizing communication protocols, this approach contributes to safer and more efficient autonomous transportation systems.