自动驾驶车辆在交叉口的通行模式研究
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摘要
以GPS/DR/MM组合定位技术和车路协同技术为基础,构建自动驾驶车辆通过交叉口的3种通行模式,分别为信号灯自适应通行模式、车车自组织通行模式和基于车路协同的中央协调通行模式。这3种通行模式可以减少车辆延误、避免交通冲突、减少交通事故、提高交通运行效率,实现自动驾驶车辆在交叉口的无障碍通行。根据车辆到达率、临界速度和平均车辆间距等参数,构造间隙移动模型和圆周轨迹模型,计算自动驾驶车辆3种通行模式下交叉口的通行能力。
With the applications of GPS/DR/MM integrated positioning technology and vehicle infrastructure integration technology, three traffic patterns with which intelligent vehicles go through the intersection are put forward. These three traffic patterns include signal lights adaptive traffic pattern, vehicle self-organizing traffic pattern and vehicle infrastructure integration traffic pattern. They can reduce vehicle delay, avoid traffic conflicts, decrease the probability of traffic accidents, improve traffic efficiency and realize the barrier-free accessibility of intelligent vehicles to the intersection. According to the parameters such as vehicles arrival rate, critical velocity and the average vehicle spacing, a clearance movement model and a circular path model are established respectively in view of these three traffic patterns.
With the applications of GPS/DR/MM integrated positioning technology and vehicle infrastructure integration technology, three traffic patterns with which intelligent vehicles go through the intersection are put forward. These three traffic patterns include signal lights adaptive traffic pattern, vehicle self-organizing traffic pattern and vehicle infrastructure integration traffic pattern. They can reduce vehicle delay, avoid traffic conflicts, decrease the probability of traffic accidents, improve traffic efficiency and realize the barrier-free accessibility of intelligent vehicles to the intersection. According to the parameters such as vehicles arrival rate, critical velocity and the average vehicle spacing, a clearance movement model and a circular path model are established respectively in view of these three traffic patterns.
引文
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[2]Fiosins M,Fiosina J,Müller J,et al.Agent-Based Integrated Decision Making for Autonomous Vehicles in Urban Traffic[M].USA:Springer Berlin Heidelberg,2011.
[3]Carlino D,Depinet M,Khandelwal P,et al.Approximately Orchestrated Routing and Transportation Analyzer:Large-scale traffic simulation for autonomous vehicles[C].USA:Intelligent Transportation Systems(ITSC),2012 15th International IEEE Conference on,2012:334-339.
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[5]Roncoli C,Papageorgiou M,Papamichail I.Traffic flow optimisation in presence of vehicle automation and communication systems-Part II:Optimal control for multi-lane motorways[J].Transportation Research Part C:Emerging Technologies(S0968-090X),2015,57:260-275.
[6]徐鑫.基于GPS的车辆定位监控系统的设计与实现[D].成都:电子科技大学,2005.Xu Xin.GPS-based Design and Implementation of Vehicle Monitoring System[D].Chengdu:University of Electronic Science and Technology,2005.
[7]李杰.车载GPS/航位推算(DR)/电子地图组合导航系统[D].太原:华北工学院,2002.Li Jie.Car GPS/Dead Reckoning(DR)/Map matching(MM)Integrated Navigation System[D].Taiyuan:North China Institute of Technology,2002.
[8]王志刚.车载导航GPS/DR/MM组合定位技术的研究[D].武汉:武汉大学,2005.Wang Zhigang.Integrated Positioning Research about GPS/DR/MM[D].Wuhan:Master’s Degree Thesis of Wuhan University,2005.
[9]程远会.基于ITS的自动驾驶车辆技术研究[D].武汉:武汉理工大学,2004.Cheng Yuanhui.Research on Automatic Vehicle Based on ITS Technology[D].Wuhan:Wuhan University of Technology,2004.
[10]王云鹏,易振国,夏海英,等.基于流行病模型的车路协同预警信息交互方法[J].北京航空航天大学学报,2011,37(5):515-518.Wang Yunpeng,Yi Zhenguo,Xia Haiying,et al.Based on Epidemiological Model of Cooperative Vehicles and Infrastructure information exchange method[J].Journal of Beihang University,2011,37(5):515-518.
[11]孙一飞.多自动驾驶车辆系统的多车协调和协作算法仿真研究[D].上海:上海交通大学,2007.Sun Yifei.Research on Multi-vehicle Coordination and Collaborative Algorithm Simulation of Multi-Automatic Vehicle[D].Shanghai:Shanghai Jiao Tong University,2007.
[12]Rui Sun,Jianming Hu,Xudong Xie,et al.Variable Speed Limit Design to Relieve Traffic Congestion Based on Cooperative Vehicle Infrastructure System[J].ProcediaSocial and Behavioral Sciences(S1877-0428),2014,138:427-438.