Planning and decision-making technology at intersections is a comprehensive research problem in intelligent transportation systems due to the uncertainties caused by a variety of traffic participants.As wireless commu...Planning and decision-making technology at intersections is a comprehensive research problem in intelligent transportation systems due to the uncertainties caused by a variety of traffic participants.As wireless communication advances,vehicle infrastructure integrated algorithms designed for intersection planning and decision-making have received increasing attention.In this paper,the recent studies on the planning and decision-making technologies at intersections are primarily overviewed.The general planning and decision-making approaches are presented,which include graph-based approach,prediction base approach,optimization-based approach and machine learning based approach.Since connected autonomous vehicles(CAVs)is the future direction for the automated driving area,we summarized the evolving planning and decision-making methods based on vehicle infrastructure cooperative technologies.Both four-way signalized and unsignalized intersection(s)are investigated under purely automated driving traffic and mixed traffic.The study benefit from current strategies,protocols,and simulation tools to help researchers identify the presented approaches’challenges and determine the research gaps,and several remaining possible research problems that need to be solved in the future.展开更多
Autonomous driving technology faces significant safety challenges,particularly at unsignalized intersections.Centralized cooperative methods have been developed to manage the flow of connected and automated vehicles.H...Autonomous driving technology faces significant safety challenges,particularly at unsignalized intersections.Centralized cooperative methods have been developed to manage the flow of connected and automated vehicles.However,many existing approaches depend on basic control algorithms,leading to lengthy inference time and suboptimal solutions.Consequently,real-time performance,road resource utilization,and traffic efficiency are compromised.While some studies have integrated reinforcement learning(RL)techniques to address these issues,they often comprise safety due to reward-driven optimization and the oversimplification of traffic scenarios,such as designing specific flow directions.These limitations raise concerns about their real-world applicability and safety.To address these shortcomings,this paper introduces a novel behavior-constrained proximal policy optimization(BCPPO)method for RL-based cooperative vehicle control at intersections.First,the problem is formulated as a multi-agent RL task within a Markov Game(MG)framework.A multi-agent proximal policy optimization(MAPPO)algorithm is proposed to handle the complex cooperative dynamics among multiple agents.The policy network employs a Long Short-Term Memory(LSTM)encoder to capture extensive social interaction information among the agents.Second,intersection control problem is formalized within the MG framework,and a safety-enhanced cooperative vehicle control strategy,BCPPO,is proposed.This method integrates formal safety verification and behavior constraints into the training and deployment of MAPPO to ensure safety and robustness.Finally,extensive simulation experiments are conducted across various intersection scenarios to evaluate the performance of BCPPO against RL-based proximal policy optimization(PPO),the rule-based first-come-first-served(FCFS)method,and the optimal control(OC)-based vehiclesintersection control system(VICS).The results demonstrate that BCPPO achieves a zero-collision rate during deployment and enhances driving comfort by 60.75%,compared to the non-safety-aware PPO method,which has a collision rate of about 13.85%.Furthermore,BCPPO improves traffic efficiency by 16.15%in comparison to FCFS and reduces inference time by a factor of 71.73 relative to the VICS method.展开更多
文摘Planning and decision-making technology at intersections is a comprehensive research problem in intelligent transportation systems due to the uncertainties caused by a variety of traffic participants.As wireless communication advances,vehicle infrastructure integrated algorithms designed for intersection planning and decision-making have received increasing attention.In this paper,the recent studies on the planning and decision-making technologies at intersections are primarily overviewed.The general planning and decision-making approaches are presented,which include graph-based approach,prediction base approach,optimization-based approach and machine learning based approach.Since connected autonomous vehicles(CAVs)is the future direction for the automated driving area,we summarized the evolving planning and decision-making methods based on vehicle infrastructure cooperative technologies.Both four-way signalized and unsignalized intersection(s)are investigated under purely automated driving traffic and mixed traffic.The study benefit from current strategies,protocols,and simulation tools to help researchers identify the presented approaches’challenges and determine the research gaps,and several remaining possible research problems that need to be solved in the future.
文摘Autonomous driving technology faces significant safety challenges,particularly at unsignalized intersections.Centralized cooperative methods have been developed to manage the flow of connected and automated vehicles.However,many existing approaches depend on basic control algorithms,leading to lengthy inference time and suboptimal solutions.Consequently,real-time performance,road resource utilization,and traffic efficiency are compromised.While some studies have integrated reinforcement learning(RL)techniques to address these issues,they often comprise safety due to reward-driven optimization and the oversimplification of traffic scenarios,such as designing specific flow directions.These limitations raise concerns about their real-world applicability and safety.To address these shortcomings,this paper introduces a novel behavior-constrained proximal policy optimization(BCPPO)method for RL-based cooperative vehicle control at intersections.First,the problem is formulated as a multi-agent RL task within a Markov Game(MG)framework.A multi-agent proximal policy optimization(MAPPO)algorithm is proposed to handle the complex cooperative dynamics among multiple agents.The policy network employs a Long Short-Term Memory(LSTM)encoder to capture extensive social interaction information among the agents.Second,intersection control problem is formalized within the MG framework,and a safety-enhanced cooperative vehicle control strategy,BCPPO,is proposed.This method integrates formal safety verification and behavior constraints into the training and deployment of MAPPO to ensure safety and robustness.Finally,extensive simulation experiments are conducted across various intersection scenarios to evaluate the performance of BCPPO against RL-based proximal policy optimization(PPO),the rule-based first-come-first-served(FCFS)method,and the optimal control(OC)-based vehiclesintersection control system(VICS).The results demonstrate that BCPPO achieves a zero-collision rate during deployment and enhances driving comfort by 60.75%,compared to the non-safety-aware PPO method,which has a collision rate of about 13.85%.Furthermore,BCPPO improves traffic efficiency by 16.15%in comparison to FCFS and reduces inference time by a factor of 71.73 relative to the VICS method.
