Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Sinc...Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.展开更多
To improve the driving efficiency of unmanned vehicles in a complex urban traffic flow environment and the safety and passenger comfort of vehicles when changing lanes,we propose a hierarchical reinforcement learning(...To improve the driving efficiency of unmanned vehicles in a complex urban traffic flow environment and the safety and passenger comfort of vehicles when changing lanes,we propose a hierarchical reinforcement learning(HRL)-based vehicle trajectory planning and tracking method.First,we present a hierarchical control framework for vehicle trajectory tracking that is based on deep reinforcement learning(DRL)and model predictive control(MPC).We design an upper-level decision model based on the trust region policy optimization algorithm integrated with long short-term memory to obtain more accurate strategies.Second,to improve stability and passenger comfort,we constructed a lower controller that combines the Bezier curve fitting method and an MPC controller.Finally,the proposed method was simulated via the car learning to act(CARLA)simulator,which is based on an unreal engine.Random urban traffic-flow test scenarios were used to simulate a real urban road-traffic environment.The simulation results illustrate that the proposed method can complete the vehicle trajectory planning and tracking task well.Compared with the existing RL methods,our proposed method has the lowest collision rate of 1.5%and achieves an average speed improvement of 7.04%.Moreover,our proposed method has better comfort performance and lower fuel consumption during the driving process.展开更多
基金supported in part by the National Natural Science Foundation of China(61603154,61773343,61621002,61703217)the Natural Science Foundation of Zhejiang Province(LY15F030021,LY19F030014)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(ICT1800407)
文摘Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.
基金supported in part by the Jiaxing Public Welfare Research Program(Grant No.2023AY11034)the Zhejiang Provincial Natural Science Foundation of China under(Grant No.LTGS23F030002)+1 种基金the National Natural Science Foundation of China(Grant No.61603154)the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(Grant No.ICT2022B52).
文摘To improve the driving efficiency of unmanned vehicles in a complex urban traffic flow environment and the safety and passenger comfort of vehicles when changing lanes,we propose a hierarchical reinforcement learning(HRL)-based vehicle trajectory planning and tracking method.First,we present a hierarchical control framework for vehicle trajectory tracking that is based on deep reinforcement learning(DRL)and model predictive control(MPC).We design an upper-level decision model based on the trust region policy optimization algorithm integrated with long short-term memory to obtain more accurate strategies.Second,to improve stability and passenger comfort,we constructed a lower controller that combines the Bezier curve fitting method and an MPC controller.Finally,the proposed method was simulated via the car learning to act(CARLA)simulator,which is based on an unreal engine.Random urban traffic-flow test scenarios were used to simulate a real urban road-traffic environment.The simulation results illustrate that the proposed method can complete the vehicle trajectory planning and tracking task well.Compared with the existing RL methods,our proposed method has the lowest collision rate of 1.5%and achieves an average speed improvement of 7.04%.Moreover,our proposed method has better comfort performance and lower fuel consumption during the driving process.
