This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous drivi...This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.展开更多
5G技术发展使用户能够通过车联网和V2X(vehicle to everything)技术快速获取周围物理环境的信息。车辆、电网运营商等也能根据路网信息进行更好的资源分配与调度,从而促进现代化智慧交通、智慧城市等发展战略的实现。考虑到现代化城市...5G技术发展使用户能够通过车联网和V2X(vehicle to everything)技术快速获取周围物理环境的信息。车辆、电网运营商等也能根据路网信息进行更好的资源分配与调度,从而促进现代化智慧交通、智慧城市等发展战略的实现。考虑到现代化城市不同区域承担着不同功能,在车辆停靠、电网容量、土地约束、成本价格等方面具有不同特征,为优化充电站部署,建立了V2X辅助下城市区域特征差异充电站模型。考虑实际情况,引入M/M/S/K排队模型和用户充电决策模型对用户行为进行刻画。进一步建立优化模型,在用地面积、电网容量以及服务需求的约束下,通过充电站点选择和充电桩部署,最大化运营商收益。为求解该问题,设计了一种基于站点容量和用户充电行为的充电站网络规划方法,首先求解给定站点的最优充电桩部署数目,然后对候选站点进行筛选聚合实现充电站网络优化。仿真验证了所提优化方法的有效性,所提充电站网络优化方法能够有效提高站点内充电桩利用率,减少运营商建设成本,提升运营商整体盈利。展开更多
基金funded by Chongqing Science and Technology Bureau (No.cstc2021jsyj-yzysbAX0008)Chongqing University of Arts and Sciences (No.P2021JG13)2021 Humanities and Social Sciences Program of Chongqing Education Commission (No.21SKGH227).
文摘This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.
文摘5G技术发展使用户能够通过车联网和V2X(vehicle to everything)技术快速获取周围物理环境的信息。车辆、电网运营商等也能根据路网信息进行更好的资源分配与调度,从而促进现代化智慧交通、智慧城市等发展战略的实现。考虑到现代化城市不同区域承担着不同功能,在车辆停靠、电网容量、土地约束、成本价格等方面具有不同特征,为优化充电站部署,建立了V2X辅助下城市区域特征差异充电站模型。考虑实际情况,引入M/M/S/K排队模型和用户充电决策模型对用户行为进行刻画。进一步建立优化模型,在用地面积、电网容量以及服务需求的约束下,通过充电站点选择和充电桩部署,最大化运营商收益。为求解该问题,设计了一种基于站点容量和用户充电行为的充电站网络规划方法,首先求解给定站点的最优充电桩部署数目,然后对候选站点进行筛选聚合实现充电站网络优化。仿真验证了所提优化方法的有效性,所提充电站网络优化方法能够有效提高站点内充电桩利用率,减少运营商建设成本,提升运营商整体盈利。
