Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and efficiently.This paper proposes a reinforcement learning(RL)method for autonomous vehicles to naviga...Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and efficiently.This paper proposes a reinforcement learning(RL)method for autonomous vehicles to navigate unsignalized intersections safely and efficiently.The method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning.A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them.A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at intersections.The algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization problem.The proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning efficiency.Simulated intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic conditions.The algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.展开更多
With the support of Vehicle-to-Everything(V2X)technology and computing power networks,the existing intersection traffic order is expected to benefit from efficiency improvements and energy savings by new schemes such ...With the support of Vehicle-to-Everything(V2X)technology and computing power networks,the existing intersection traffic order is expected to benefit from efficiency improvements and energy savings by new schemes such as de-signalization.How to effectively manage autonomous vehicles for traffic control with high throughput at unsignalized intersections while ensuring safety has been a research hotspot.This paper proposes a collision-free autonomous vehicle scheduling framework based on edge-cloud computing power networks for unsignalized intersections where the lanes entering the intersections are undirectional,and designs an efficient communication system and protocol.First,by analyzing the collision point occupation time,this paper formulates an absolute value programming problem.Second,this problem is solved with low complexity by the Edge Intelligence Optimal Entry Time(EI-OET)algorithm based on edge-cloud computing power support.Then,the communication system and protocol are designed for the proposed scheduling scheme to realize efficient and low-latency vehicular communications.Finally,simulation experiments compare the proposed scheduling framework with directional and traditional traffic light scheduling mechanisms,and the experimental results demonstrate its high efficiency,low latency,and low complexity.展开更多
To investigate bicyclists＇ behavior at unsignalized intersections with mixed traffic flow, a bicycle capacity model of borrowed-priority merge was developed by the addition-conflict-flow procedure. Based on the actua...To investigate bicyclists＇ behavior at unsignalized intersections with mixed traffic flow, a bicycle capacity model of borrowed-priority merge was developed by the addition-conflict-flow procedure. Based on the actual traffic situation, the concept of borrowed priority, in which the majorroad bicycles borrow the priority of major-road cars to enter the intersections when consecutive headway for major-steam cars is lower than the critical gap for minor-road cars, was addressed. Bicycle capacity at a typical unsignalized intersection is derived by the addition-conflict-flow procedure. The proposes model was validated by the empirical investigation. Numerical results show that bicycle capacity at an intersection is the function of major-road and minor-road car streams. Bicycle capacity increases with increasing major-road cars but decreases with increasing minorroad cars.展开更多
In order to describe the time-headway distribution more precisely in urban traffic network,the mixed distribution model was introduced which has been widely used in mathematical statistics,and a capacity model of unsi...In order to describe the time-headway distribution more precisely in urban traffic network,the mixed distribution model was introduced which has been widely used in mathematical statistics,and a capacity model of unsignalized intersections was obtained based on gap acceptance theory.The new model is suitable for absolute and limited priority controlled conditions and can be regarded as a more general form which handles simple headway distributions including lognormal distribution,negative exponential distribution and shifted negative exponential distribution.Through analyses of the main influencing factors in this model,the proportion of free flowing and the standard variance of gaps between any two continuous following vehicles are high sensitivity with the capacity when major stream volume is low.Besides,the capacity is affected deeply by the mean value of following vehicle gaps when major stream value is fixed and the proportion of free flowing is small.At last,the observed minor stream capacity is obtained by the survey date in Changchun city,and the average relative error between the theoretical capacity proposed in this paper is 13.73%,meanwhile the accuracy increases by 16.68% compared with the theoretical value when major stream obeys shifted negative exponential distribution.展开更多
In India, traffic flow on roads is highly mixed in nature with wide variations in the static and dynamic characteristics of vehicles. At unsignalized intersections, vehicles generally do not follow lane discipline and...In India, traffic flow on roads is highly mixed in nature with wide variations in the static and dynamic characteristics of vehicles. At unsignalized intersections, vehicles generally do not follow lane discipline and ignore the rules of priority. Drivers generally become more aggressive and tend to cross the uncontrolled intersections without considering the conflicting traffic. All these conditions cause a very complex traffic situation at unsignal- ized intersections which have a great impact on the capacity and performance of traffic intersections. A new method called additive conflict flow （ACF） method is suitable to determine the capacity of unsignalized inter- sections in non-lane-based mixed traffic conditions as prevailing in India. Occupation time is the key parameter for ACF method, which is defined as the time spent by a vehicle in the conflict area at the intersection. Data for this study were collected at two three-legged unsignalized intersections （one is uncontrolled and other one is semi- controlled） in Mangalore city, India using video-graphic technique during peak periods on three consecutive week days. The occupation time of vehicles at these intersections were studied and compared. The data on conflicting traffic volume and occupation time by each subject vehicle at the conflict area were extracted from the videos using image processing software. The subject vehicles were divided into three categories： two wheelers,cars, and auto-rickshaws. Mathematical relationships were developed to relate the occupation time of different cate- gories of vehicles with the conflicting flow of vehicles for various movements at both the intersections. It was found that occupation time increases with the increasing con- flicting traffic and observed to be higher at the uncontrolled intersection compared to the semicontrolled intersec- tion. The segregated turning movements and the presence of mini roundabout at the semicontrolled intersection reduces the conflicts of vehicular movements, which ulti- mately reduces the occupation time. The proposed methodology will be useful to determine the occupation time for various movements at unsignalized intersections. The models developed in the study can be used by practitioners and traffic engineers to estimate the capacity of unsignalized intersections in non-lane-based discipline and mixed traffic conditions.展开更多
基金supported by the National Natural Science Foundation of China (52102394,52172384)Hunan Provincial Natural Science Foundation of China (2023JJ10008)Young Elite Scientists Sponsorship Program by CAST (2022QNRC001)。
文摘Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and efficiently.This paper proposes a reinforcement learning(RL)method for autonomous vehicles to navigate unsignalized intersections safely and efficiently.The method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning.A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them.A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at intersections.The algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization problem.The proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning efficiency.Simulated intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic conditions.The algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.
基金supported by the Natural Science Fund for Distinguished Young Scholars of Jiangsu Province under Grant BK20220067。
文摘With the support of Vehicle-to-Everything(V2X)technology and computing power networks,the existing intersection traffic order is expected to benefit from efficiency improvements and energy savings by new schemes such as de-signalization.How to effectively manage autonomous vehicles for traffic control with high throughput at unsignalized intersections while ensuring safety has been a research hotspot.This paper proposes a collision-free autonomous vehicle scheduling framework based on edge-cloud computing power networks for unsignalized intersections where the lanes entering the intersections are undirectional,and designs an efficient communication system and protocol.First,by analyzing the collision point occupation time,this paper formulates an absolute value programming problem.Second,this problem is solved with low complexity by the Edge Intelligence Optimal Entry Time(EI-OET)algorithm based on edge-cloud computing power support.Then,the communication system and protocol are designed for the proposed scheduling scheme to realize efficient and low-latency vehicular communications.Finally,simulation experiments compare the proposed scheduling framework with directional and traditional traffic light scheduling mechanisms,and the experimental results demonstrate its high efficiency,low latency,and low complexity.
基金Supported by the National Basic Research Program of China (2012CB725400)the National Natural Science Foundation of China(70901005+2 种基金7107101671131001)Fundamental Research Funds for the Central Universities(2011JBM055)
文摘To investigate bicyclists＇ behavior at unsignalized intersections with mixed traffic flow, a bicycle capacity model of borrowed-priority merge was developed by the addition-conflict-flow procedure. Based on the actual traffic situation, the concept of borrowed priority, in which the majorroad bicycles borrow the priority of major-road cars to enter the intersections when consecutive headway for major-steam cars is lower than the critical gap for minor-road cars, was addressed. Bicycle capacity at a typical unsignalized intersection is derived by the addition-conflict-flow procedure. The proposes model was validated by the empirical investigation. Numerical results show that bicycle capacity at an intersection is the function of major-road and minor-road car streams. Bicycle capacity increases with increasing major-road cars but decreases with increasing minorroad cars.
基金Sponsored by the National High Technology Research and Development Program of China(Grant No.2011AA110304)the National Natural Science Foundation of China(Grant No.50908100,70971053)
文摘In order to describe the time-headway distribution more precisely in urban traffic network,the mixed distribution model was introduced which has been widely used in mathematical statistics,and a capacity model of unsignalized intersections was obtained based on gap acceptance theory.The new model is suitable for absolute and limited priority controlled conditions and can be regarded as a more general form which handles simple headway distributions including lognormal distribution,negative exponential distribution and shifted negative exponential distribution.Through analyses of the main influencing factors in this model,the proportion of free flowing and the standard variance of gaps between any two continuous following vehicles are high sensitivity with the capacity when major stream volume is low.Besides,the capacity is affected deeply by the mean value of following vehicle gaps when major stream value is fixed and the proportion of free flowing is small.At last,the observed minor stream capacity is obtained by the survey date in Changchun city,and the average relative error between the theoretical capacity proposed in this paper is 13.73%,meanwhile the accuracy increases by 16.68% compared with the theoretical value when major stream obeys shifted negative exponential distribution.
文摘In India, traffic flow on roads is highly mixed in nature with wide variations in the static and dynamic characteristics of vehicles. At unsignalized intersections, vehicles generally do not follow lane discipline and ignore the rules of priority. Drivers generally become more aggressive and tend to cross the uncontrolled intersections without considering the conflicting traffic. All these conditions cause a very complex traffic situation at unsignal- ized intersections which have a great impact on the capacity and performance of traffic intersections. A new method called additive conflict flow （ACF） method is suitable to determine the capacity of unsignalized inter- sections in non-lane-based mixed traffic conditions as prevailing in India. Occupation time is the key parameter for ACF method, which is defined as the time spent by a vehicle in the conflict area at the intersection. Data for this study were collected at two three-legged unsignalized intersections （one is uncontrolled and other one is semi- controlled） in Mangalore city, India using video-graphic technique during peak periods on three consecutive week days. The occupation time of vehicles at these intersections were studied and compared. The data on conflicting traffic volume and occupation time by each subject vehicle at the conflict area were extracted from the videos using image processing software. The subject vehicles were divided into three categories： two wheelers,cars, and auto-rickshaws. Mathematical relationships were developed to relate the occupation time of different cate- gories of vehicles with the conflicting flow of vehicles for various movements at both the intersections. It was found that occupation time increases with the increasing con- flicting traffic and observed to be higher at the uncontrolled intersection compared to the semicontrolled intersec- tion. The segregated turning movements and the presence of mini roundabout at the semicontrolled intersection reduces the conflicts of vehicular movements, which ulti- mately reduces the occupation time. The proposed methodology will be useful to determine the occupation time for various movements at unsignalized intersections. The models developed in the study can be used by practitioners and traffic engineers to estimate the capacity of unsignalized intersections in non-lane-based discipline and mixed traffic conditions.
