A study based on lane change intention recognition and deep reinforcement learning was conducted for the decision-making of highway on-ramps.Firstly,the study identified the lane-changing intentions of vehicles based on the characteristic parameters of the target vehicle and its surrounding vehicles,while also designing the environmental state space.Secondly,a mixed action space was developed that incorporates both discrete lane-changing behaviors and continuous longitudinal acceleration and deceleration behaviors of the vehicle.Subsequently,an environmental reward function was formulated based on four key aspects:vehicle ride comfort,traffic efficiency,driving safety,and successful merging.In consideration of the characteristics inherent in ramp merging and lane-changing tasks,environmental termination conditions were established.Finally,a ramp merging simulation platform was constructed,utilizing actual road data and the Simulation of Urban MObility framework,to validate the feasibility of the proposed model.The results of multiple comparative analysis experiments indicate that the proposed method outperforms others in terms of collision rate and success rate,and the model demonstrates a superior success rate across varying road traffic density conditions.In the future,more robust decision-making will be carried out for different scenarios.
维普
万方数据