Lane-changing decision-making model for connected and automated vehicles based on MSIF-DRL in a mixed traffic environment
HAN Lei
ZHANG Lun
GUO Weian
Abstract：Existing lane-changing decision-making models for Connected and Automated Vehicles(CAV)lack robustness,pose safety concerns and rely primarily on the ego-vehicle's information and limited sensor data,making it challenging to infer optimal actions in mixed environments with CAV and Human-Driven Vehicles(HDV).To this end,this paper introduces an end-to-end lane-changing decision model for CAV in mixed traffic environments based on Multi-Source Information Fusion Deep Reinforcement Learning(MSIF-DRL).It considers information from the sensor data,ego-vehicle,and Vehicle-to-Vehicle(V2V)communication within the CAV's upstream and downstream range.Firstly,a state space containing multi-source information is constructed,with the information from different vehicles assigned weights.Secondly,various dynamic multi-source information is en-coded into a high-dimensional feature space through the encoding network for information fusion to ob-tain the feature map.Then,the feature map is flattened and fed into the dueling deep double Q net-work with prioritized experience replay for action selection and evaluation.Finally,a reward function applicable to the mainline and on-ramp CAV is designed to guide the proposed MSIF-DRL model,solving the discretionary and mandatory lane change problems of CAV in freeway merging scenarios.Through simulation experiments using SUMO software under various conditions,the proposed MSIF-DRL model is compared with existing models to verify its effectiveness.The experimental re-sults show that,compared to the existing models,the proposed MSIF-DRL model achieves the high-est rewards,successful lane-change rate,successful merge rate,average driving speed,ride comfort,and the lowest collision risk under different simulation scenarios.The proposed model increases the successful lane change rate,successful merge rate,and average driving speed by 29.17%,27.71%,and 17.43%,respectively.Furthermore,as the penetration rate increases,the MSIF-DRL model ex-hibits enhanced performance and robustness in addressing CAV's lane-changing decisions in mixed traffic environments.
Keywords：intelligent transportationconnected and automated drivingdeep reinforcement learninglane-changing decision-making modelmulti-source information fusionmixed traffic flow
Publication Date：2023-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：14( 148-161 )