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
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 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
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
Cooperative driving is widely viewed as a promising method to better utilize limited road resources and alleviate traffic congestion.In recent years,several cooperative driving approaches for idealized traffic scenari...Cooperative driving is widely viewed as a promising method to better utilize limited road resources and alleviate traffic congestion.In recent years,several cooperative driving approaches for idealized traffic scenarios(i.e.,uniform vehicle arrivals,lengths,and speeds)have been proposed.However,theoretical analyses and comparisons of these approaches are lacking.In this study,we propose a unified group-by-group zipper-style movement model to describe different approaches synthetically and evaluate their performance.We derive the maximum throughput for cooperative driving plans of idealized unsignalized intersections and discuss how to minimize the delay of vehicles.The obtained conclusions shed light on future cooperative driving studies.展开更多
Unsignalized intersections are identified as the critical locations due to higher number of road crashes at these locations.The primary causes of crashes at unsignalized intersections are limited sight distance,incorr...Unsignalized intersections are identified as the critical locations due to higher number of road crashes at these locations.The primary causes of crashes at unsignalized intersections are limited sight distance,incorrect assessment of gaps by drivers on the minor road,and higher speeds of vehicles on the major road.In an effort to improve safety and reduce the severity of crashes at unsignalized intersections,an intelligent transportation system(ITS)called the intersection conflict warning system(ICWS)has been developed.The ICWS consists of an activated warning sign and sensors that detect vehicles approaching the intersection.This paper aims to summarize the performance evaluation of ICWS that has been published in the literature.The review commences with an overview of the purpose of designing and installing ICWS at unsignalized intersections.It then discusses driving performance measures considered to evaluate the effectiveness of ICWS in three different environments,i.e.,real world,driving simulator,and simulations,and their results are presented.The paper also discusses the type of sensors used to detect vehicles approaching the intersection and their accuracy in vehicle detection.The results reveal that ICWS had substantially improved driver behaviour.In the presence of ICWS,drivers resulted in shorter reaction time,lower approach speed,increased critical gap acceptance,and reduction in conflicts.An improvement in the performance of ICWS can be achieved by educating drivers about the sign,maintaining system reliability,and further examining the effect of various traffic factors,driver factors,and environmental factors on ICWS.The findings of this study can help researchers and engineers to design a better ICWS that can greatly enhance driving performance and safety at unsignalized intersections.展开更多
Safe and smooth interaction between other vehicles is one of the ultimate goals of driving automation.However,recent reports of demonstrative deployments of automated vehicles(AVs)indicate that AVs are still difficult...Safe and smooth interaction between other vehicles is one of the ultimate goals of driving automation.However,recent reports of demonstrative deployments of automated vehicles(AVs)indicate that AVs are still difficult to meet the expecta-tion of other interacting drivers,which leads to several AV accidents involving human-driven vehicles(HVs)without the understanding about the dynamic interaction process.By investigating 4300 video clips of traffic accidents,it is found that the limited dynamic visual field of drivers is one leading factor in inter-vehicle interaction accidents.A game-theoretic decision algorithm considering social compatibility is proposed to handle the interaction with a human-driven truck at an unsignalized intersection.Starting from a probabilistic model for the visual field characteristics of truck drivers,social fit-ness and reciprocal altruism in the decision are incorporated in the game payoff design.Human-in-the-loop experiments are carried out,in which 24 subjects are invited to drive and interact with AVs deployed with the proposed algorithm and two comparison algorithms.Totally,207 cases of intersection interactions are obtained and analyzed,which shows that the proposed decision-making algorithm can improve both safety and time efficiency,and make AV decisions more in line with the expectation of interacting human drivers.These findings can help inform the design of automated driving decision algorithms,to ensure that AVs can be safely and efficiently integrated into the human-dominated traffic.展开更多
Many studies suggest that more crashes occur due to mixed traffic flow at unsignalized intersections. However, very little is known about the injury severity of these crashes. The objective of this study is therefore ...Many studies suggest that more crashes occur due to mixed traffic flow at unsignalized intersections. However, very little is known about the injury severity of these crashes. The objective of this study is therefore to investigate how contributory factors affect crash injury severity at unsignalized intersections. The dataset used for this analysis derived from police crash reports from Dec. 2006 to Apr. 2009 in Heilongjiang Province, China. An ordered probit model was developed to predict the probability that the injury severity of a crash will be one of four levels ： no injury, slight injury, severe injury, and fatal injury. The injury severity of a crash was evaluated in terms of the most severe injury sustained by any person involved in the crash. Results from the present study showed that different factors had varying effects on crash injury severity. Factors found to result in the increased probability of serious injuries include adverse weather, sideswiping with pedestrians on poor surface, the interaction of rear-ends and the third-class highway, winter night without illumination, and the interaction between traffic signs or markings and the third-class highway. Although there are some limitations in the current study, this study provides more insights into crash injury severity at unsignalized intersections.展开更多
The Washington,DC crash statistic report for the period from 2013 to 2015 shows that the city recorded about 41789 crashes at unsignalized intersections,which resulted in 14168 injuries and 51 fatalities.The economic ...The Washington,DC crash statistic report for the period from 2013 to 2015 shows that the city recorded about 41789 crashes at unsignalized intersections,which resulted in 14168 injuries and 51 fatalities.The economic cost of these fatalities has been estimated to be in the millions of dollars.It is therefore necessary to investigate the predictability of the occurrence of theses crashes,based on pertinent factors,in order to provide mitigating measures.This research focused on the development of models to predict the injury severity of crashes using support vector machines(SVMs)and Gaussian naïve Bayes classifiers(GNBCs).The models were developed based on 3307 crashes that occurred from 2008 to 2015.Eight SVM models and a GNBC model were developed.The most accurate model was the SVM with a radial basis kernel function.This model predicted the severity of an injury sustained in a crash with an accuracy of approximately 83.2%.The GNBC produced the worst-performing model with an accuracy of 48.5%.These models will enable transport officials to identify crash-prone unsignalized intersections to provide the necessary countermeasures beforehand.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 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.
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.
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.
基金This work was supported by the National Natural Science Foundation of China(No.52272420)the Science and Technology Innovation Committee of Shenzhen(No.CJGJZD20200617102801005)the Tsinghua-Toyota Joint Research Institution.
文摘Cooperative driving is widely viewed as a promising method to better utilize limited road resources and alleviate traffic congestion.In recent years,several cooperative driving approaches for idealized traffic scenarios(i.e.,uniform vehicle arrivals,lengths,and speeds)have been proposed.However,theoretical analyses and comparisons of these approaches are lacking.In this study,we propose a unified group-by-group zipper-style movement model to describe different approaches synthetically and evaluate their performance.We derive the maximum throughput for cooperative driving plans of idealized unsignalized intersections and discuss how to minimize the delay of vehicles.The obtained conclusions shed light on future cooperative driving studies.
基金supported by the project“M2Smart:Smart Cities for Emerging Countries based on Sensing Network and Big Data Analysis of Multimodal Regional Transport System”,JST/JICA SATREPS,Japan。
文摘Unsignalized intersections are identified as the critical locations due to higher number of road crashes at these locations.The primary causes of crashes at unsignalized intersections are limited sight distance,incorrect assessment of gaps by drivers on the minor road,and higher speeds of vehicles on the major road.In an effort to improve safety and reduce the severity of crashes at unsignalized intersections,an intelligent transportation system(ITS)called the intersection conflict warning system(ICWS)has been developed.The ICWS consists of an activated warning sign and sensors that detect vehicles approaching the intersection.This paper aims to summarize the performance evaluation of ICWS that has been published in the literature.The review commences with an overview of the purpose of designing and installing ICWS at unsignalized intersections.It then discusses driving performance measures considered to evaluate the effectiveness of ICWS in three different environments,i.e.,real world,driving simulator,and simulations,and their results are presented.The paper also discusses the type of sensors used to detect vehicles approaching the intersection and their accuracy in vehicle detection.The results reveal that ICWS had substantially improved driver behaviour.In the presence of ICWS,drivers resulted in shorter reaction time,lower approach speed,increased critical gap acceptance,and reduction in conflicts.An improvement in the performance of ICWS can be achieved by educating drivers about the sign,maintaining system reliability,and further examining the effect of various traffic factors,driver factors,and environmental factors on ICWS.The findings of this study can help researchers and engineers to design a better ICWS that can greatly enhance driving performance and safety at unsignalized intersections.
基金supported by the Department of Science and Technology of Zhejiang under Grants 2022C01241 and 2023C01238supported by a student project from Scientific Research Fund of Zhejiang Provincial Education Department(Y202250796).
文摘Safe and smooth interaction between other vehicles is one of the ultimate goals of driving automation.However,recent reports of demonstrative deployments of automated vehicles(AVs)indicate that AVs are still difficult to meet the expecta-tion of other interacting drivers,which leads to several AV accidents involving human-driven vehicles(HVs)without the understanding about the dynamic interaction process.By investigating 4300 video clips of traffic accidents,it is found that the limited dynamic visual field of drivers is one leading factor in inter-vehicle interaction accidents.A game-theoretic decision algorithm considering social compatibility is proposed to handle the interaction with a human-driven truck at an unsignalized intersection.Starting from a probabilistic model for the visual field characteristics of truck drivers,social fit-ness and reciprocal altruism in the decision are incorporated in the game payoff design.Human-in-the-loop experiments are carried out,in which 24 subjects are invited to drive and interact with AVs deployed with the proposed algorithm and two comparison algorithms.Totally,207 cases of intersection interactions are obtained and analyzed,which shows that the proposed decision-making algorithm can improve both safety and time efficiency,and make AV decisions more in line with the expectation of interacting human drivers.These findings can help inform the design of automated driving decision algorithms,to ensure that AVs can be safely and efficiently integrated into the human-dominated traffic.
基金supported by the National Natural Science Foundation of China(No.51178149)
文摘Many studies suggest that more crashes occur due to mixed traffic flow at unsignalized intersections. However, very little is known about the injury severity of these crashes. The objective of this study is therefore to investigate how contributory factors affect crash injury severity at unsignalized intersections. The dataset used for this analysis derived from police crash reports from Dec. 2006 to Apr. 2009 in Heilongjiang Province, China. An ordered probit model was developed to predict the probability that the injury severity of a crash will be one of four levels ： no injury, slight injury, severe injury, and fatal injury. The injury severity of a crash was evaluated in terms of the most severe injury sustained by any person involved in the crash. Results from the present study showed that different factors had varying effects on crash injury severity. Factors found to result in the increased probability of serious injuries include adverse weather, sideswiping with pedestrians on poor surface, the interaction of rear-ends and the third-class highway, winter night without illumination, and the interaction between traffic signs or markings and the third-class highway. Although there are some limitations in the current study, this study provides more insights into crash injury severity at unsignalized intersections.
文摘The Washington,DC crash statistic report for the period from 2013 to 2015 shows that the city recorded about 41789 crashes at unsignalized intersections,which resulted in 14168 injuries and 51 fatalities.The economic cost of these fatalities has been estimated to be in the millions of dollars.It is therefore necessary to investigate the predictability of the occurrence of theses crashes,based on pertinent factors,in order to provide mitigating measures.This research focused on the development of models to predict the injury severity of crashes using support vector machines(SVMs)and Gaussian naïve Bayes classifiers(GNBCs).The models were developed based on 3307 crashes that occurred from 2008 to 2015.Eight SVM models and a GNBC model were developed.The most accurate model was the SVM with a radial basis kernel function.This model predicted the severity of an injury sustained in a crash with an accuracy of approximately 83.2%.The GNBC produced the worst-performing model with an accuracy of 48.5%.These models will enable transport officials to identify crash-prone unsignalized intersections to provide the necessary countermeasures beforehand.
