Eco-Friendly On-Ramp Merging Strategy for Connected and Automated Vehicles in Heterogeneous Traffic

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY(2023)

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摘要
The on-ramp area usually produces congestion, high energy consumption, and high emission. In order to improve the efficiency of multi-lane heterogeneous on-ramp traffic composed of vehicles with different dynamic characteristics, we propose an eco-friendly on-ramp merging strategy for connected and automated vehicles in heterogeneous traffic. Firstly, the optimal lane decision method is proposed via the conditional proximal policy optimization algorithm to optimize the traffic flow of each lane and avoid local congestion in heterogeneous traffic. Then, considering travel time, energy consumption, and emission, the eco-friendly merging optimization problem is established to optimize vehicles' longitudinal velocity. According to the optimal lane and the optimal longitudinal velocity profile, the lateral end-point and longitudinal end-point of the local trajectory are solved for planning the local trajectory of the vehicles. Each vehicle tracks its local trajectory, realizing eco-friendly on-ramp merging. Compared with two existing ramp merging methods, results show that the proposed algorithm can mitigate local congestion effectively, and has superiority in traffic efficiency, energy economy, and eco-friendliness.
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关键词
Merging,Optimization,Trajectory,Energy consumption,Reinforcement learning,Vehicle dynamics,Roads,On-ramp merging,heterogeneous traffic,connected and automated vehicles (CAVs),reinforcement learning
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