A distributed deep reinforcement learning-based longitudinal control strategy for connected automated vehicles combining attention mechanism

With the rapid development of connected automated vehicles (CAVs), the trajectory control of CAVs has become a focus in traffic engineering. This paper proposes a distributed deep reinforcement learning-based longitudinal control strategy for CAVs combining attention mechanism, which enhances the stability of mixed traffic, car-following efficiency, energy efficiency, and safety. A longitudinal control strategy is built using a deep reinforcement learning model. The CAVs gradually learn optimal car-following strategy in training process to improve safety, stability, fuel economy, mobility, and driving comfort. To further capture the interactions among vehicles in each platoon, the graph attention network is introduced to facilitate the car-following control strategy. To verify the effectiveness of the proposed method, a comparative analysis is conducted, which indicates that the proposed method can dramatically dampen oscillations, enhance traffic efficiency, reduce fuel consumption, and improve driving safety under different scenarios.