Highlights A multi-agent deep reinforcement learning framework has been proposed to alleviate bottleneck congestion. The experimental scenarios are based on a real-world mandatory lane-changing experiment. Both the longitudinal and lateral accelerations of CAVs are continuously controlled. The safety of the lane-changing process is quantified using a newly proposed safety metric. The introduced 2D collision avoidance strategy outperforms traditional models.
Abstract Bottleneck areas are prone to severe traffic congestion due to the sudden drop in capacity. To improve traffic efficiency in the bottleneck area, this paper proposes a multi-agent deep reinforcement learning framework integrating collision avoidance strategies to improve traffic efficiency in a mandatory lane change scenario. The proposed method considers distance-keeping and lane-changing coordination in a connected autonomous vehicle (CAV) environment, by controlling vehicles' longitudinal and lateral movement to effectively reduce traffic congestion in a mandatory lane change scenario. This framework was trained and tested in a simulation environment that is the same as the natural driving environment. Compared with real-world data and the benchmark model (a Dueling Double Deep Q-Network-based model), the proposed model shows better performance in terms of average speed, travel time, throughput, and safety in the bottleneck area. The results show that the proposed model can effectively reduce traffic congestion and improve traffic efficiency in a mandatory lane change scenario.
A multi-agent reinforcement learning-based longitudinal and lateral control of CAVs to improve traffic efficiency in a mandatory lane change scenario
2023-11-27
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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