Leveraging Transfer Learning with Federated DRL for Autonomous Vehicles Platooning

The emergence of Autonomous Vehicles has ushered in a new era of transportation efficacy and safety. Platooning, which involves vehicles traveling closely in sync, offers potential for mitigating traffic congestion, decreasing fuel usage, and improving road safety. However, realizing platooning’s full potential requires robust control strategies adaptable to various conditions. This study explores integrating Federated Deep Reinforcement Learning (FDRL) into AV platooning systems to enhance control and efficiency. It focuses on leveraging FDRL to optimize platoon behavior while considering AV’s distributed nature. Specifically, the proposed approach involves training Deep Reinforcement Learning (DRL) model locally on individual vehicles (Agents) within a platoon, allowing them to adapt and learn from local data and experiences. The trained model is then transferred to other platoons via 5 G infrastructure, improving overall performance. Simulation studies demonstrate the superiority of FDRL over other methods, suggesting its potential to advance AV platooning systems in future transportation landscapes.