Human-like Merging Control of Intelligent Connected Vehicles on the Acceleration Lane
To develop a merging control algorithm for intelligent connected vehicles(ICVs)on freeway acceleration lanes interacting with human-driven vehicles(HDVs)on the mainline,we propose a merging control model(DQN-RF).This model integrates the deep Q-network(DQN)algorithm and the random forest(RF)algorithm.First,a roadside data acquisition platform was established to collect the naturalistic merging behavior data of HDVs at a typical merging zone with an acceleration lane on the G70 freeway in China.Second,a human-like merging decision model using RF was built using historical merging environmental contextual data and the merging urgency of the merging vehicle on the acceleration lane as input.We constructed a simulated merging scenario featuring an acceleration lane on the freeway using the simulation of urban mobility(SUMO)platform.Utilizing the Python language,we developed a testing script environment for the deep reinforcement learning algorithm.Additionally,we introduced a longitudinal acceleration control algorithm based on DQN.Finally,the DQN-RF merging control model,which embedded the RF merging decision algorithm into the DQN longitudinal acceleration control algorithm,was established to embrace merging decision control and longitudinal acceleration control in a comprehensive framework.The default lane-changing control algorithm in SUMO,known as"LC2013,"was also combined with the proposed DQN algorithm to serve as a baseline model.The simulation results show that,with the same acceleration action value space[-1,2]m·s-2,compared to the DQN-LC2013 model,the DQN-RF model receives a higher total reward value.The average accelerations of the ICV for the DQN-RF and DQN-LC2013 models are 0.55 and 0.09 m·s-2,respectively.Furthermore,the average speeds are 21.4 and 19.7 m·s-1,respectively.There are no stop-and-wait phenomena observed when the DQN-RF model is adopted,while there are seven stop-and-wait events in 100 turns of simulation when the DQN-LC2013 model is adopted.The proposed DQN-RF merging control model can realize human-like merging decisions and improve the merging efficiency and success rate of the ICV.The DQN-RF model can be used for merging decision control and longitudinal acceleration control of the ICVs on the freeway acceleration lane.
traffic engineeringmerging control modelDQN-RFintelligent connected vehicleacceleration laneSUMO simulation