Road intersection is one of the most complex and accident-prone traffic scenarios,so it’s challenging for autonomous vehicles(AVs)to make safe and efficient decisions at the intersections.Most of the related studies ...Road intersection is one of the most complex and accident-prone traffic scenarios,so it’s challenging for autonomous vehicles(AVs)to make safe and efficient decisions at the intersections.Most of the related studies focus on the solution to a single scenario or only guarantee safety without considering driving efficiency.To address these problems,this study proposed a deep reinforcement learning enabled decision-making framework for AVs to drive through intersections automatically,safely and efficiently.The mapping relationship between traffic images and vehicle operations was obtained by an end-to-end decision-making framework established by convolutional neural networks.Traffic images collected at two timesteps were used to calculate the relative velocity between vehicles.Markov decision process was employed to model the interaction between AVs and other vehicles,and the deep Q-network algorithm was utilized to obtain the optimal driving policy regarding safety and efficiency.To verify the effectiveness of the proposed decision-making framework,the top three accident-prone crossing path crash scenarios at intersections were simulated,when different initial vehicle states were adopted for better generalization capability.The results showed that the developed method could make AVs drive safely and efficiently through intersections in all of the tested scenarios.展开更多
Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada...Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.展开更多
The rapid advance of autonomous vehicles(AVs)has motivated new perspectives and potential challenges for existing modes of transportation.Currently,driving assistance systems of Level 3 and below have been widely prod...The rapid advance of autonomous vehicles(AVs)has motivated new perspectives and potential challenges for existing modes of transportation.Currently,driving assistance systems of Level 3 and below have been widely produced,and several applications of Level 4 systems to specific situations have also been gradually developed.By improving the automation level and vehicle intelligence,these systems can be further advanced towards fully autonomous driving.However,general development concepts for Level 5 AVs remain unclear,and the existing methods employed in the development processes of Levels 0-4 have been mainly based on task-driven function development related to specific scenarios.Therefore,it is difficult to identify the problems encountered by high-level AVs.The essential logical and physical mechanisms of vehicles have hindered further progression towards Level 5 systems.By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essence of driving,we put forward a coordinated and balanced framework based on the brain-cerebellum-organ concept through reasoning and deduction.Based on a mixed mode relying on the crow inference and parrot imitation approach,we explore the research paradigm of autonomous learning and prior knowledge to realize the characteristics of self-learning,self-adaptation,and self-transcendence for AVs.From a systematic,unified,and balanced point of view and based on least action principles and unified safety field concepts,we aim to provide a novel research concept and develop an effective approach for the research and development of high-level AVs,specifically at Level 5.展开更多
基金This work is supported by the National Natural Science Foundation of China(Grant No.51805332)the Young Elite Scientists Sponsorship Program funded by the China Society of Automotive Engineers,the Natural Science Foundation of Guangdong Province(Grant No.2018A030310532)the Shenzhen Fundamental Research Fund(Grant No.JCYJ20190808142613246).
文摘Road intersection is one of the most complex and accident-prone traffic scenarios,so it’s challenging for autonomous vehicles(AVs)to make safe and efficient decisions at the intersections.Most of the related studies focus on the solution to a single scenario or only guarantee safety without considering driving efficiency.To address these problems,this study proposed a deep reinforcement learning enabled decision-making framework for AVs to drive through intersections automatically,safely and efficiently.The mapping relationship between traffic images and vehicle operations was obtained by an end-to-end decision-making framework established by convolutional neural networks.Traffic images collected at two timesteps were used to calculate the relative velocity between vehicles.Markov decision process was employed to model the interaction between AVs and other vehicles,and the deep Q-network algorithm was utilized to obtain the optimal driving policy regarding safety and efficiency.To verify the effectiveness of the proposed decision-making framework,the top three accident-prone crossing path crash scenarios at intersections were simulated,when different initial vehicle states were adopted for better generalization capability.The results showed that the developed method could make AVs drive safely and efficiently through intersections in all of the tested scenarios.
文摘Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.
基金This work was jointly supported by the National Science Fund for Distinguished Young Scholars(51625503)the National Natural Science Foundation of China,the Major Project(61790561)the Joint Laboratory for Internet of Vehicle,Ministry of Education,China Mobile Communications Corporation.
文摘The rapid advance of autonomous vehicles(AVs)has motivated new perspectives and potential challenges for existing modes of transportation.Currently,driving assistance systems of Level 3 and below have been widely produced,and several applications of Level 4 systems to specific situations have also been gradually developed.By improving the automation level and vehicle intelligence,these systems can be further advanced towards fully autonomous driving.However,general development concepts for Level 5 AVs remain unclear,and the existing methods employed in the development processes of Levels 0-4 have been mainly based on task-driven function development related to specific scenarios.Therefore,it is difficult to identify the problems encountered by high-level AVs.The essential logical and physical mechanisms of vehicles have hindered further progression towards Level 5 systems.By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essence of driving,we put forward a coordinated and balanced framework based on the brain-cerebellum-organ concept through reasoning and deduction.Based on a mixed mode relying on the crow inference and parrot imitation approach,we explore the research paradigm of autonomous learning and prior knowledge to realize the characteristics of self-learning,self-adaptation,and self-transcendence for AVs.From a systematic,unified,and balanced point of view and based on least action principles and unified safety field concepts,we aim to provide a novel research concept and develop an effective approach for the research and development of high-level AVs,specifically at Level 5.
