Transportation systems serve as a crucial foundation for maintaining the normal operation of cities and satisfying the requirements of public life.With the development of next-generation information technologies,autom...Transportation systems serve as a crucial foundation for maintaining the normal operation of cities and satisfying the requirements of public life.With the development of next-generation information technologies,automated driving technologies have brought new opportunities to improve the performance of traffic systems and the intelligence level of cities.Currently,significant research efforts have been conducted to develop automated driving systems in three major industries,i.e.,automobile,roadway,and telecommunication.However,the collaboration and integration of automated driving systems among automobile,roadway,and telecommunications are still lacking,especially for collaborative development of system architecture and objectives.To address the need,this study first proposes a system architecture of vehicle-road-cloud collaborative automated driving system(VRC-CADS).Three levels of collaborative development,i.e.,collaborative sensing,collaborative decision-making,and collaborative control,are designed for the VRC-CADS.Based on that,the typical scenarios of automated driving for each level of the system are further defined and interpreted.Moreover,feasible and systematic suggestions for the collaborative development of the VRC-CADS are provided,considering the cross-cutting collaboration among government agencies,academia,and industry.The proposed system architecture of the VRC-CADS will facilitate the optimization of urban lifelines and the evolution of intelligent cities.展开更多
Vehicle-road-cloud integrated systems hold considerable potential for enhancing driving performance and traffic efficiency.However,current approaches often lack a unified framework that fully capitalises on this integ...Vehicle-road-cloud integrated systems hold considerable potential for enhancing driving performance and traffic efficiency.However,current approaches often lack a unified framework that fully capitalises on this integration for intelligent connected vehicles(ICVs).To address this gap,this paper introduces a novel cloud control system for ICVs.We detail the system's concept,architecture,and operating characteristics based on cyber-physical systems(CPS)theory.The system's efficacy is demonstrated in an eco-driving control scenario involving multiple signalised intersections,with the goal of minimising fuel consumption.For the planning layer,the eco-driving optimal control problem,incorporating space-time constraints,is formulated and solved using a direct multiple shooting method.For the control layer,a tube model predictive control(TMPC)framework with a receding horizon strategy is implemented to ensure robustness against external disturbances.Simulation experiments validate the proposed method,showing significant improvements in fuel efficiency over baseline methods.展开更多
Roadside cameras play a crucial role in road traffic,serving as an indispensable part of integrated vehicleroad-cloud systems due to their extensive visibility and monitoring capabilities.Nevertheless,these cameras fa...Roadside cameras play a crucial role in road traffic,serving as an indispensable part of integrated vehicleroad-cloud systems due to their extensive visibility and monitoring capabilities.Nevertheless,these cameras face challenges in continuously tracking targets across perception domains.To address the issue of tracking vehicles across nonoverlapping perception domains between cameras,we propose a cross-camera vehicle tracking method within a Vehicle–Road–Cloud system that integrates visual and spatiotemporal information.A Gaussian model with microlevel traffic features is trained using vehicle information obtained through online tracking.Finally,the association of vehicle targets is achieved through the Gaussian model combining time and visual feature information.The experimental results indicate that the proposed system demonstrates excellent performance.展开更多
Vehicle-Road-Cloud Integration system(VRCIS)requires high-precision vehicle positioning and tracking,with very low system latency,which is a difficult task given the quantity and quality of data.To this end,a distribu...Vehicle-Road-Cloud Integration system(VRCIS)requires high-precision vehicle positioning and tracking,with very low system latency,which is a difficult task given the quantity and quality of data.To this end,a distributed computing framework using multi-access edge computing(MEC)devices is proposed in this paper.To process trajectory data(including preprocessing,calibration,multi-sensor trajectory matching,and trajectory prediction),as well as integrate machine learning algorithms to improve the accuracy of trajectory prediction,especially for complex and diverse driving scenarios environmental conditions,a framework is designed.In addition,to conduct a comprehensive evaluation of the overall performance of trajectory tracking,factors such as trajectory smoothness and velocity consistency—components of our novel evaluation metrics—are considered.Experiments show that the framework can continuously track tens of thousands of vehicles on highway,with average longitudinal and lateral errors of 2.14 and 0.84 m respectively,with average speed error of 1.91 kph.The experiments on large-scale road networks with 1,777 sensors are implemented,with continuous multi-vehicle tracking over 157 km of highway,and establishing superior performance compared to existing methods.Furthermore,processing latency remained below 340 ms,demonstrating the potential of this framework to enhance driver experience,improve road safety and efficiency.展开更多
文摘Transportation systems serve as a crucial foundation for maintaining the normal operation of cities and satisfying the requirements of public life.With the development of next-generation information technologies,automated driving technologies have brought new opportunities to improve the performance of traffic systems and the intelligence level of cities.Currently,significant research efforts have been conducted to develop automated driving systems in three major industries,i.e.,automobile,roadway,and telecommunication.However,the collaboration and integration of automated driving systems among automobile,roadway,and telecommunications are still lacking,especially for collaborative development of system architecture and objectives.To address the need,this study first proposes a system architecture of vehicle-road-cloud collaborative automated driving system(VRC-CADS).Three levels of collaborative development,i.e.,collaborative sensing,collaborative decision-making,and collaborative control,are designed for the VRC-CADS.Based on that,the typical scenarios of automated driving for each level of the system are further defined and interpreted.Moreover,feasible and systematic suggestions for the collaborative development of the VRC-CADS are provided,considering the cross-cutting collaboration among government agencies,academia,and industry.The proposed system architecture of the VRC-CADS will facilitate the optimization of urban lifelines and the evolution of intelligent cities.
基金supported by National Natural Science Foundation of China under grant 52302410,the China Postdoctoral Science Foundation under Grant 2024T170489Postdoctoral Fellowship Program of CPSF under grant GZB20230354+1 种基金Research and development of autonomous driving domain controller and its algorithm under grant 2023Z070Young Elite Scientists Sponsorship Program by CHINA-SAE,and Shuimu Tsinghua Scholarship.
文摘Vehicle-road-cloud integrated systems hold considerable potential for enhancing driving performance and traffic efficiency.However,current approaches often lack a unified framework that fully capitalises on this integration for intelligent connected vehicles(ICVs).To address this gap,this paper introduces a novel cloud control system for ICVs.We detail the system's concept,architecture,and operating characteristics based on cyber-physical systems(CPS)theory.The system's efficacy is demonstrated in an eco-driving control scenario involving multiple signalised intersections,with the goal of minimising fuel consumption.For the planning layer,the eco-driving optimal control problem,incorporating space-time constraints,is formulated and solved using a direct multiple shooting method.For the control layer,a tube model predictive control(TMPC)framework with a receding horizon strategy is implemented to ensure robustness against external disturbances.Simulation experiments validate the proposed method,showing significant improvements in fuel efficiency over baseline methods.
基金the National Natural Science Foundation of China(52172389)Natural Science Foundation of Guangdong Province(2022A1515012080)Tsinghua-Toyota Joint Research Institute Interdisciplinary Program.
文摘Roadside cameras play a crucial role in road traffic,serving as an indispensable part of integrated vehicleroad-cloud systems due to their extensive visibility and monitoring capabilities.Nevertheless,these cameras face challenges in continuously tracking targets across perception domains.To address the issue of tracking vehicles across nonoverlapping perception domains between cameras,we propose a cross-camera vehicle tracking method within a Vehicle–Road–Cloud system that integrates visual and spatiotemporal information.A Gaussian model with microlevel traffic features is trained using vehicle information obtained through online tracking.Finally,the association of vehicle targets is achieved through the Gaussian model combining time and visual feature information.The experimental results indicate that the proposed system demonstrates excellent performance.
基金National Natural Science Foundation of China(Grant Number:52172389)National Key Research and Development Program of China(Grant Number:2022YFB2503202)Alibaba Innovative Research Program.
文摘Vehicle-Road-Cloud Integration system(VRCIS)requires high-precision vehicle positioning and tracking,with very low system latency,which is a difficult task given the quantity and quality of data.To this end,a distributed computing framework using multi-access edge computing(MEC)devices is proposed in this paper.To process trajectory data(including preprocessing,calibration,multi-sensor trajectory matching,and trajectory prediction),as well as integrate machine learning algorithms to improve the accuracy of trajectory prediction,especially for complex and diverse driving scenarios environmental conditions,a framework is designed.In addition,to conduct a comprehensive evaluation of the overall performance of trajectory tracking,factors such as trajectory smoothness and velocity consistency—components of our novel evaluation metrics—are considered.Experiments show that the framework can continuously track tens of thousands of vehicles on highway,with average longitudinal and lateral errors of 2.14 and 0.84 m respectively,with average speed error of 1.91 kph.The experiments on large-scale road networks with 1,777 sensors are implemented,with continuous multi-vehicle tracking over 157 km of highway,and establishing superior performance compared to existing methods.Furthermore,processing latency remained below 340 ms,demonstrating the potential of this framework to enhance driver experience,improve road safety and efficiency.
