Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumpti...Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.展开更多
A critical safe distance(CSD)model in V2V(vehicle-to-vehicle)communication systems was proposed to primarily enhance driving safety by disseminating warning notifications to vehicles when they approach calculated CSD....A critical safe distance(CSD)model in V2V(vehicle-to-vehicle)communication systems was proposed to primarily enhance driving safety by disseminating warning notifications to vehicles when they approach calculated CSD.By elaborately analyzing the vehicular movement features especially when braking,our CSD definition was introduced and its configuration method was given through dividing radio range into different communication zones.Based on our definition,the needed message propagation delay was also derived which could be used to control the beacon frequency or duration.Next,the detailed CSD expressions were proposed in different mobility scenarios by fully considering the relative movement status between the front and rear vehicles.Numerical results show that our proposed model could provide reasonable CSD under different movement scenarios which eliminates the unnecessary reserved inter-vehicle distance and guarantee the safety at the same time.The compared time-headway model always shows a smaller CSD due to focusing on traffic efficiency whereas the traditional braking model generally outputs a larger CSD because it assumes that the following car drives with a constant speed and did not discuss the scenario when the leading car suddenly stops.Different from these two models,our proposed model could well balances the requirements between driving safety and traffic throughput efficiency by generating a CSD in between the values of the two models in most cases.展开更多
针对常规线性卡尔曼滤波越来越不能满足多机动目标跟踪精度需求的问题,提出一种基于自适应多模型粒子滤波的协同跟踪方法.首先,主车和协同车分别执行自适应交互式多模型粒子滤波(adaptive interactive multi model particle filter,AIMM...针对常规线性卡尔曼滤波越来越不能满足多机动目标跟踪精度需求的问题,提出一种基于自适应多模型粒子滤波的协同跟踪方法.首先,主车和协同车分别执行自适应交互式多模型粒子滤波(adaptive interactive multi model particle filter,AIMM-PF)算法,获得环境中目标车辆的运动状态;其次,协同车通过车车通信将跟踪到的目标状态发送给主车;最后,利用基于匈牙利算法和快速协方差交叉算法的数据关联和数据融合技术实现多机动目标的协同跟踪.搭建了V2V通信、雷达和定位仿真系统,选定两辆智能车作为主车和协同车,感知并跟踪200 m范围内的7辆目标车,进行了仿真试验.结果表明,与传统的单车跟踪相比,协同跟踪扩大了感知范围,且在不影响跟踪效率的情况下使跟踪误差降低了31.1%.展开更多
为研究无人车编队系统的编队保持、队形重构及队形变换功能,提出一种混合式领航跟随策略,以降低对领航车的依赖并确保编队完整性。开发基于车间(Vehicle to Vehicle,V2V)通信的跟随车独立避障功能,并设计了实时管理编队成员属性并支持...为研究无人车编队系统的编队保持、队形重构及队形变换功能,提出一种混合式领航跟随策略,以降低对领航车的依赖并确保编队完整性。开发基于车间(Vehicle to Vehicle,V2V)通信的跟随车独立避障功能,并设计了实时管理编队成员属性并支持人机交互的编队节点管理系统。提出一种三维空间下的三次样条曲线动态扩展轨迹规划方法,结合V2V通信获取前车位姿信息生成跟随轨迹并实现避障。利用Frenet坐标系解耦车距保持与轨迹跟踪问题,采用PID控制器和线性二次调节(Linear Quadratic Regulator,LQR)控制器分别进行纵向控制和横向轨迹跟踪。研究结果表明:所搭建的仿真环境可快速验证方法性能,显示该方法具有良好的性能;通过实车验证了车辆编队系统的3种功能,通过车距稳定保持,证实所提方法具备良好实时性,能够实现多车编队的有效跟随,通过多种队形的变换以及成员入队离队场景,显示出高度的智能拓展性和适应性。展开更多
基金supported in part by Australian Research Council Discovery Early Career Researcher Award(DE210100273)。
文摘Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.
基金Project(20100481323) supported by China Postdoctoral Science FoundationProjects(61201133,61172055,61072067,51278058)supported by the National Natural Science Foundation of China+4 种基金Project(NCET-11-0691) supported by the Program for New Century Excellent Talents in UniversityProject(11105) supported by the Foundation of Guangxi Key Lab of Wireless Wideband Communication & Signal Processing,ChinaProject(B08038) supported by the "111" Project,ChinaProject(K5051301011) supported by the Fundamental Research Funds for the Central Universities of ChinaProject(CX12178(6)) supported by the Xian Municipal Technology Transfer Promotion funds,China
文摘A critical safe distance(CSD)model in V2V(vehicle-to-vehicle)communication systems was proposed to primarily enhance driving safety by disseminating warning notifications to vehicles when they approach calculated CSD.By elaborately analyzing the vehicular movement features especially when braking,our CSD definition was introduced and its configuration method was given through dividing radio range into different communication zones.Based on our definition,the needed message propagation delay was also derived which could be used to control the beacon frequency or duration.Next,the detailed CSD expressions were proposed in different mobility scenarios by fully considering the relative movement status between the front and rear vehicles.Numerical results show that our proposed model could provide reasonable CSD under different movement scenarios which eliminates the unnecessary reserved inter-vehicle distance and guarantee the safety at the same time.The compared time-headway model always shows a smaller CSD due to focusing on traffic efficiency whereas the traditional braking model generally outputs a larger CSD because it assumes that the following car drives with a constant speed and did not discuss the scenario when the leading car suddenly stops.Different from these two models,our proposed model could well balances the requirements between driving safety and traffic throughput efficiency by generating a CSD in between the values of the two models in most cases.
文摘针对常规线性卡尔曼滤波越来越不能满足多机动目标跟踪精度需求的问题,提出一种基于自适应多模型粒子滤波的协同跟踪方法.首先,主车和协同车分别执行自适应交互式多模型粒子滤波(adaptive interactive multi model particle filter,AIMM-PF)算法,获得环境中目标车辆的运动状态;其次,协同车通过车车通信将跟踪到的目标状态发送给主车;最后,利用基于匈牙利算法和快速协方差交叉算法的数据关联和数据融合技术实现多机动目标的协同跟踪.搭建了V2V通信、雷达和定位仿真系统,选定两辆智能车作为主车和协同车,感知并跟踪200 m范围内的7辆目标车,进行了仿真试验.结果表明,与传统的单车跟踪相比,协同跟踪扩大了感知范围,且在不影响跟踪效率的情况下使跟踪误差降低了31.1%.
文摘为研究无人车编队系统的编队保持、队形重构及队形变换功能,提出一种混合式领航跟随策略,以降低对领航车的依赖并确保编队完整性。开发基于车间(Vehicle to Vehicle,V2V)通信的跟随车独立避障功能,并设计了实时管理编队成员属性并支持人机交互的编队节点管理系统。提出一种三维空间下的三次样条曲线动态扩展轨迹规划方法,结合V2V通信获取前车位姿信息生成跟随轨迹并实现避障。利用Frenet坐标系解耦车距保持与轨迹跟踪问题,采用PID控制器和线性二次调节(Linear Quadratic Regulator,LQR)控制器分别进行纵向控制和横向轨迹跟踪。研究结果表明:所搭建的仿真环境可快速验证方法性能,显示该方法具有良好的性能;通过实车验证了车辆编队系统的3种功能,通过车距稳定保持,证实所提方法具备良好实时性,能够实现多车编队的有效跟随,通过多种队形的变换以及成员入队离队场景,显示出高度的智能拓展性和适应性。
