This study aims to construct a virtual twin testing framework for the safety of the intended functionality of intelligent connected vehicles to address the safety requirements of intelligent driving and transportation...This study aims to construct a virtual twin testing framework for the safety of the intended functionality of intelligent connected vehicles to address the safety requirements of intelligent driving and transportation systems.The research methods include the construction of a theoretical model of safety for intelligent connected vehicles based on the concept of virtual twins,the correlation study between key concepts and functional safety,and the application research of virtual twin technology in the safety testing of intelligent connected vehicles.The results reveal that the virtual twin testing framework can effectively enhance the functional safety of intelligent connected vehicles,reduce development costs,and shorten the product launch cycle.The conclusion suggests that this framework provides strong support for the healthy development of the intelligent connected vehicle industry and has a positive impact on the safety and efficiency of intelligent transportation systems.展开更多
With the development of technology,the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal.The data of ICV(intelligent connected vehicles)is ...With the development of technology,the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal.The data of ICV(intelligent connected vehicles)is the key to organically maximizing their efficiency.However,in the context of increasingly strict global data security supervision and compliance,numerous problems,including complex types of connected vehicle data,poor data collaboration between the IT(information technology)domain and OT(operation technology)domain,different data format standards,lack of shared trust sources,difficulty in ensuring the quality of shared data,lack of data control rights,as well as difficulty in defining data ownership,make vehicle data sharing face a lot of problems,and data islands are widespread.This study proposes FADSF(Fuzzy Anonymous Data Share Frame),an automobile data sharing scheme based on blockchain.The data holder publishes the shared data information and forms the corresponding label storage on the blockchain.The data demander browses the data directory information to select and purchase data assets and verify them.The data demander selects and purchases data assets and verifies them by browsing the data directory information.Meanwhile,this paper designs a data structure Data Discrimination Bloom Filter(DDBF),making complaints about illegal data.When the number of data complaints reaches the threshold,the audit traceability contract is triggered to punish the illegal data publisher,aiming to improve the data quality and maintain a good data sharing ecology.In this paper,based on Ethereum,the above scheme is tested to demonstrate its feasibility,efficiency and security.展开更多
With the development of vehicles towards intelligence and connectivity,vehicular data is diversifying and growing dramatically.A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle(ICV)...With the development of vehicles towards intelligence and connectivity,vehicular data is diversifying and growing dramatically.A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle(ICV)applications are proposed for the dispersed computing network composed of heterogeneous task vehicles and Network Computing Points(NCPs).Considering the amount of task data and the idle resources of NCPs,a computing resource scheduling model for NCPs is established.Taking the heterogeneous task execution delay threshold as a constraint,the optimization problem is described as the problem of maximizing the utilization of computing resources by NCPs.The proposed problem is proven to be NP-hard by using the method of reduction to a 0-1 knapsack problem.A many-to-many matching algorithm based on resource preferences is proposed.The algorithm first establishes the mutual preference lists based on the adaptability of the task requirements and the resources provided by NCPs.This enables the filtering out of un-schedulable NCPs in the initial stage of matching,reducing the solution space dimension.To solve the matching problem between ICVs and NCPs,a new manyto-many matching algorithm is proposed to obtain a unique and stable optimal matching result.The simulation results demonstrate that the proposed scheme can improve the resource utilization of NCPs by an average of 9.6%compared to the reference scheme,and the total performance can be improved by up to 15.9%.展开更多
This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control fram...This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.展开更多
The electrification of vehicle helps to improve its operation efficiency and safety.Due to fast development of network,sensors,as well as computing technology,it becomes realizable to have vehicles driving autonomousl...The electrification of vehicle helps to improve its operation efficiency and safety.Due to fast development of network,sensors,as well as computing technology,it becomes realizable to have vehicles driving autonomously.To achieve autonomous driving,several steps,including environment perception,path-planning,and dynamic control,need to be done.However,vehicles equipped with on-board sensors still have limitations in acquiring necessary environmental data for optimal driving decisions.Intelligent and connected vehicles(ICV)cloud control system(CCS)has been introduced as a new concept as it is a potentially synthetic solution for high level automated driving to improve safety and optimize traffic flow in intelligent transportation.This paper systematically investigated the concept of cloud control system from cloud related applications on ICVs,and cloud control system architecture design,as well as its core technologies development.Based on the analysis,the challenges and suggestions on cloud control system development have been addressed.展开更多
Intelligent and connected vehicles have leveraged edge computing paradigm to enhance their environment comprehension and behavior planning capabilities.As the quantity of intelligent vehicles and the demand for edge c...Intelligent and connected vehicles have leveraged edge computing paradigm to enhance their environment comprehension and behavior planning capabilities.As the quantity of intelligent vehicles and the demand for edge computing are increasing rapidly,it becomes critical to efficiently orchestrate the communication and computation resources on edge clouds.Existing methods usually perform resource allocation in a fairly effective but still reactive manner,which is subject to the capacity of nearby edge clouds.To deal with the contradiction between the spatiotemporally varying demands for edge computing and the fixed edge cloud capacity,we proactively balance the edge computing demands across edge clouds by appropriate route planning.In this paper,route planning and resource allocation are jointly optimized to enhance intelligent driving.We propose a multi-scale decentralized optimization method to deal with the curse of dimensionality.In large-scale optimization,backpressure algorithm is used to conduct route planning and load balancing across edge clouds.In small-scale optimization,game-theoretic multi-agent learning is exploited to perform regional resource allocation.The experimental results show that the proposed algorithm outperforms the baseline algorithms which optimize route planning and resource allocation separately.展开更多
The concept of connected vehicles is with great potentials for enhancing the road transportation systems in the future. To support the functions and applications under the connected vehicles frame, the estimation of d...The concept of connected vehicles is with great potentials for enhancing the road transportation systems in the future. To support the functions and applications under the connected vehicles frame, the estimation of dynamic states of the vehicles under the cooperative environments is a fundamental issue. By integrating multiple sensors, localization modules in OBUs(on-board units) require effective estimation solutions to cope with various operation conditions. Based on the filtering estimation framework for sensor fusion, an ensemble Kalman filter(En KF) is introduced to estimate the vehicle's state with observations from navigation satellites and neighborhood vehicles, and the original En KF solution is improved by using the cubature transformation to fulfill the requirements of the nonlinearity approximation capability, where the conventional ensemble analysis operation in En KF is modified to enhance the estimation performance without increasing the computational burden significantly. Simulation results from a nonlinear case and the cooperative vehicle localization scenario illustrate the capability of the proposed filter, which is crucial to realize the active safety of connected vehicles in future intelligent transportation.展开更多
Digital twin is an essential enabling technology for 6G connected vehicles.Through highfidelity mobility simulation,digital twin is expected to make accurate prediction about the vehicle trajectory,and then support in...Digital twin is an essential enabling technology for 6G connected vehicles.Through highfidelity mobility simulation,digital twin is expected to make accurate prediction about the vehicle trajectory,and then support intelligent applications such as safety monitoring and self-driving for connected vehicles.However,it is observed that even if a digital twin model is perfectly derived,it might still fail to predict the trajectory due to tiny measurement noise or delay in the initial vehicle locations.This paper aims at investigating the sources of unpredictability of digital twin.Take the car-following behaviors in connected vehicles for case study.The theoretical analysis and experimental results indicate that the predictability of digital twin naturally depends on its system complexity.Once a system enters a complex pattern,its longterm states are unpredictable.Furthermore,our study discloses that the complexity is determined,on the one hand,by the intrinsic factors of the target physical system such as the driver’s response sensitivity and delay,and on the other hand,by the crucial parameters of the digital twin system such as the sampling interval and twining latency.展开更多
Internet of things is deemed as the one of the great revolution after the age of Industrial Revolution.With the development of the communication technology,more and more entities are connected to the communication net...Internet of things is deemed as the one of the great revolution after the age of Industrial Revolution.With the development of the communication technology,more and more entities are connected to the communication network and become one of the elements in the network.Over recent decades,in the area of intelligent transportation,pedestrian and transport infrastructure are connected to the communication network to improve the driving safety and traffic efficiency which is known as the ICV(Intelligent Connected Vehicle).This paper summarizes the global ICV progresses in the past decades and the latest activities of ICV in China,and introduces various aspects regarding the recent development of the ICV,including industry development,spectrum and standard,at the same time.展开更多
The development of intelligent connected vehicles(ICVs)has tremendously inspired the emergence of a new computing paradigm called mobile edge computing(MEC),which meets the demands of delay-sensitive on-vehicle applic...The development of intelligent connected vehicles(ICVs)has tremendously inspired the emergence of a new computing paradigm called mobile edge computing(MEC),which meets the demands of delay-sensitive on-vehicle applications.Most existing studies focusing on the issue of task offloading in ICVs assume that the MEC server can directly complete computation tasks without considering the necessity of service caching.However,this is unrealistic in practice because a large number of tasks require the use of corresponding third-party libraries and databases,that is,service caching.Therefore,we investigate the delay optimization in an MEC-enabled ICVs system with multiple mobile vehicles,resource-limited base stations(BSs),and one cloud server.We aim to determine the optimal service caching and task offloading decisions to minimize the overall system delay using mixed-integer nonlinear programming.To address this problem,we first convert it into a quadratically constrained quadratic program and then propose an efficient semidefinite relaxation-based joint service caching and task offloading(JSCTO)algorithm to obtain the service caching and task offloading decisions.In the simulations,we validate the efficiency of our proposed method by setting different numbers of vehicles and the storage capacity of BSs.The results show that our proposed JSCTO algorithm can significantly decrease the total delay of all offloaded tasks compared with the cloud processing only scheme.展开更多
As one of the typical applications of connected vehicles(CVs),the vehicle platoon control technique has been proven to have the advantages of reducing emissions,improving traffic throughout and driving safety.In this ...As one of the typical applications of connected vehicles(CVs),the vehicle platoon control technique has been proven to have the advantages of reducing emissions,improving traffic throughout and driving safety.In this paper,a unified hierarchical framework is designed for cooperative control of CVs with both heterogeneous model parameters and structures.By separating neighboring information interaction from local dynamics control,the proposed framework is designed to contain an upper-level observing layer and a lower-level tracking control layer,which helps address the heterogeneity in vehicle parameters and structures.Within the proposed framework,an observer is designed for following vehicles to observe the leading vehicle's states using neighboring communication,while a tracking controller is designed to track the observed leading vehicle using local feedback control.Closed-loop stability in the absence and presence of communication time delay is analyzed,and the observer is further extended to a finite time convergent one to address string stability under general communication topology.Numerical simulation and field experiment verify the effectiveness of the proposed method.展开更多
In this study,we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles.We designed two software modules:The rst to derive the Pearson correlation coefcients to analyze ...In this study,we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles.We designed two software modules:The rst to derive the Pearson correlation coefcients to analyze the collected data and the second to conduct exploratory data analysis of the collected vehicle data.In particular,we analyzed the dangerous driving patterns of motorists based on the safety standards of the Korea Transportation Safety Authority.We also analyzed seasonal fuel efciency(four seasons)and mileage of vehicles,and identied rapid acceleration,rapid deceleration,sudden stopping(harsh braking),quick starting,sudden left turn,sudden right turn and sudden U-turn driving patterns of vehicles.We implemented the density-based spatial clustering of applications with a noise algorithm for trajectory analysis based on GPS(Global Positioning System)data and designed a long shortterm memory algorithm and an auto-regressive integrated moving average model for time-series data analysis.In this paper,we mainly describe the development environment of the analysis software,the structure and data ow of the overall analysis platform,the conguration of the collected vehicle data,and the various algorithms used in the analysis.Finally,we present illustrative results of our analysis,such as dangerous driving patterns that were detected.展开更多
Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements...Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements.Deep reinforcement learning(DRL)exhibits excellent capability of real-time decision-making and adaptability to complex scenarios,and generalization abilities.However,it is arduous to guarantee complete driving safety and efficiency under the constraints of training samples and costs.This paper proposes a Mixture of Expert method(MoE)based on Soft Actor-Critic(SAC),where the upper-level discriminator dynamically decides whether to activate the lower-level DRL expert or the heuristic expert based on the features of the input state.To further enhance the performance of the DRL expert,a buffer zone is introduced in the reward function,preemptively applying penalties before insecure situations occur.In order to minimize collision and off-road rates,the Intelligent Driver Model(IDM)and Minimizing Overall Braking Induced by Lane changes(MOBIL)strategy are designed by heuristic experts.Finally,tested in typical simulation scenarios,MOE shows a 13.75%improvement in driving efficiency compared with the traditional DRL method with continuous action space.It ensures high safety with zero collision and zero off-road rates while maintaining high adaptability.展开更多
Pavement condition monitoring and its timely maintenance is necessary to ensure the safety and quality of the roadway infrastructure. The International Roughness Index (IRI) is a commonly used measure to quantify road...Pavement condition monitoring and its timely maintenance is necessary to ensure the safety and quality of the roadway infrastructure. The International Roughness Index (IRI) is a commonly used measure to quantify road surface roughness and is a critical input to asset management. In Indiana, the IRI statistic contributes to roughly half of the pavement quality index computation used for asset management. Most agencies inventory IRI once a year, however, pavement conditions vary much more frequently. The objective of this paper is to develop a framework using crowdsourced connected vehicle data to identify and detect temporal changes in IRI. Over 3 billion connected vehicle records in Indiana were analyzed across 30 months between 2022 and 2024 to understand the spatiotemporal variations in roughness. Annual comparisons across all major interstates in Indiana showed the miles of interstates classified as “Good” decreased from 1896 to 1661 miles between 2022 and 2024. The miles of interstate classified as “Needs Maintenance” increased from 82 to 120 miles. A detailed case study showing monthly and daily changes of estimated IRI on I-65 are presented along with supporting dashcam images. Although the crowdsourced IRI estimates are not as robust as traditional specialized pavement profilers, they can be obtained on a monthly, weekly, or even daily basis. The paper concludes by suggesting a combination of frequent crowdsourced IRI and commercially available dashcam imagery of roadway can provide an agile and responsive mechanism for agencies to implement pavement asset management programs that can complement existing annual programs.展开更多
Connected automated vehicles(CAVs)rely heavily on intelligent algorithms and remote sensors.If the control center or on-board sensors are under cyber-attack due to the security vulnerability of wireless communication,...Connected automated vehicles(CAVs)rely heavily on intelligent algorithms and remote sensors.If the control center or on-board sensors are under cyber-attack due to the security vulnerability of wireless communication,it can cause significant damage to CAVs or passengers.The primary objective of this study is to model cyberattacked traffic flow and evaluate the impacts of cyber-attack on the traffic system filled with CAVs in a connected environment.Based on the analysis on environmental perception system and possible cyber-attacks on sensors,a novel lane-changing model for CAVs is proposed and multiple traffic scenarios for cyber-attacks are designed.The impact of the proportion of cyber-attacked vehicles and the severity of the cyber-attack on the lanechanging process is then quantitatively analyzed.The evaluation indexes include spatio-temporal evolution of average speed,spatial distribution of selected lane-changing gaps,lane-changing rate distribution,lane-changing preparation search time,efficiency and safety.Finally,the numerical simulation results show that the freeway traffic near an off-ramp is more sensitive to the proportion of cyber-attacked vehicles than to the severity of the cyber-attack.Also,when the traffic system is under cyber-attack,more unsafe back gaps are chosen for lane-changing,especially in the center lane.Therefore,more lane-changing maneuvers are concentrated on approaching the off-ramp,causing severe congestions and potential rear-end collisions.In addition,as the number of cyber-attacked vehicles and the severity of cyber-attacks increase,the road capacity and safety level will rapidly decrease.The results of this study can provide a theoretical basis for accident avoidance and efficiency improvement for the design of CAVs and management of automated highway systems.展开更多
Traffic flow mobility on expressway plays an important role in urban development.With the emergent technologies,connected vehicles,including both connected automated vehicles(CAVs)and connected regular vehicles(CRVs),...Traffic flow mobility on expressway plays an important role in urban development.With the emergent technologies,connected vehicles,including both connected automated vehicles(CAVs)and connected regular vehicles(CRVs),are equipped with connectivity features to improve efficiency of urban expressway in a mixed traffic scenario.Existing research indicates that without targeted management,the integration of CAVs and CRVs into regular vehicles(RVs)traffic can lead to a series of congestion issues.A potential solution lies in the implementation of dedicated lanes,each designated for specific vehicle types,which could alleviate traffic flow complications.Therefore,this paper proposes a dynamic optimal lane management strategy for multi-lane mixed traffic urban expressway,aimed at maximizing the whole discharge flow.To begin with,we present an analytical method to mathematically derive discharge flow of each lane type including mixed traffic lane,CAV/CRV dedicated lane,and RV dedicated lane,from the perspective of both traffic demand and traffic capacity.Then,taking a three-lane mixed traffic urban expressway as an application,we conduct a numerical analysis of the dynamic optimal lane management strategy,based on comparisons among eight distinct strategies,under varying factors of traffic demands,penetration rates of CAVs and CRVs,and allocation proportions of vehicles upstream assigned to mixed traffic lanes.The results validate the effectiveness of the analytical method proposed for lane management strategies.It also indicates that the aforementioned system factors have significant impacts on the dynamic optimal lane management strategy.展开更多
With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid elec...With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.展开更多
With the advancement of connected vehicle(CV)technology,an increasing number of CVs will appear on urban roads.Data collected by CVs can be used to optimize signal parameters at intersections,thus improving traffic ef...With the advancement of connected vehicle(CV)technology,an increasing number of CVs will appear on urban roads.Data collected by CVs can be used to optimize signal parameters at intersections,thus improving traffic efficiency.In this study,we design a real-time adaptive signal control method for an arterial road with multiple intersections with low penetration rates.By utilizing vehicle arrival information collected by CVs,our method rapidly determines optimal signal phasing and timing(SPaT).The proposed adaptive signal control method was tested with the Simulation of Urban Mobility(SUMO)software,and was found to reduce total travel delay in the network better than a fixed coordination control method.The performance of the proposed method in reducing travel delay is expected to improve as CV detection range increases.展开更多
This paper presents a decentralized fuel efficient model predictive control(MPC) strategy for a group of connected vehicles incorporating vertical vibration. To capture the vehicle vibration dynamics, the dynamics of ...This paper presents a decentralized fuel efficient model predictive control(MPC) strategy for a group of connected vehicles incorporating vertical vibration. To capture the vehicle vibration dynamics, the dynamics of the suspension system is integrated with the longitudinal dynamics of the vehicle. Furthermore, a MPC framework with finite time horizon is formulated to calculate the optimal velocity profile that compromises fuel economy, mobility and ride comfort for every individual vehicle with the safety and physical constraints considered. In the MPC framework, the target velocity is calculated using signal phase and timing(SPAT)information to reduce the number of stoppage at red lights, and the vertical acceleration is calculated parallel to the calculation of the fuel consumption. The MPC optimal problem is solved with fast-MPC approach which enhances the computational efficiency via exploiting the structure of the control system and approximate methods. Simulation studies are conducted over different SPATs and connectivity penetration rates and the results validate the advantages of the proposed control architecture.展开更多
Advanced vehicular control technologies rely on accurate speed prediction to make ecological and safe decisions. This paper proposes a novel stochastic speed prediction method for connected vehicles by incorporating a...Advanced vehicular control technologies rely on accurate speed prediction to make ecological and safe decisions. This paper proposes a novel stochastic speed prediction method for connected vehicles by incorporating a Bayesian network(BN) and a Back Propagation(BP) neural network. A BN model is first designed for predicting the stochastic vehicular speed in a priori. To improve the accuracy of the BN-based speed prediction, a BP-based predicted speed error compensation module is constructed by formulating a mapping between the predicted speed and its corresponding prediction error. In the end, a filtering algorithm is developed to smoothen the compensated stochastic vehicular speed. To validate the workings of the proposed approaches in experiments, two typical scenarios are considered: one predecessor vehicle in a double-vehicle scenario and two predecessor vehicles in a multi-vehicle scenario. Simulation results under the considered scenarios demonstrate that the proposed BN-BP fusion method outperforms the BN-based method with respect to the root mean square error, standardized residuals, and R-squared, and the online prediction time of proposed fusion prediction can satisfy a real-time application requirement. The main highlighted contributions of this article are threefold:(1) We put forward an improved BN method, which is combined with a BP neural network, to construct a stochastic vehicular speed prediction method under connected driving;(2) different from existing methods, a unique interconnected framework that consists of a stochastic vehicular speed prediction module, a compensation module, and a speed smoothing module is proposed;(3) extensive simulation studies based on a set of evaluation metrics are illustrated to reveal the advantages and merits of the proposed approaches.展开更多
文摘This study aims to construct a virtual twin testing framework for the safety of the intended functionality of intelligent connected vehicles to address the safety requirements of intelligent driving and transportation systems.The research methods include the construction of a theoretical model of safety for intelligent connected vehicles based on the concept of virtual twins,the correlation study between key concepts and functional safety,and the application research of virtual twin technology in the safety testing of intelligent connected vehicles.The results reveal that the virtual twin testing framework can effectively enhance the functional safety of intelligent connected vehicles,reduce development costs,and shorten the product launch cycle.The conclusion suggests that this framework provides strong support for the healthy development of the intelligent connected vehicle industry and has a positive impact on the safety and efficiency of intelligent transportation systems.
基金This work was financially supported by the National Key Research and Development Program of China(2022YFB3103200).
文摘With the development of technology,the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal.The data of ICV(intelligent connected vehicles)is the key to organically maximizing their efficiency.However,in the context of increasingly strict global data security supervision and compliance,numerous problems,including complex types of connected vehicle data,poor data collaboration between the IT(information technology)domain and OT(operation technology)domain,different data format standards,lack of shared trust sources,difficulty in ensuring the quality of shared data,lack of data control rights,as well as difficulty in defining data ownership,make vehicle data sharing face a lot of problems,and data islands are widespread.This study proposes FADSF(Fuzzy Anonymous Data Share Frame),an automobile data sharing scheme based on blockchain.The data holder publishes the shared data information and forms the corresponding label storage on the blockchain.The data demander browses the data directory information to select and purchase data assets and verify them.The data demander selects and purchases data assets and verifies them by browsing the data directory information.Meanwhile,this paper designs a data structure Data Discrimination Bloom Filter(DDBF),making complaints about illegal data.When the number of data complaints reaches the threshold,the audit traceability contract is triggered to punish the illegal data publisher,aiming to improve the data quality and maintain a good data sharing ecology.In this paper,based on Ethereum,the above scheme is tested to demonstrate its feasibility,efficiency and security.
基金supported by the National Natural Science Foundation of China(Grant No.62072031)the Applied Basic Research Foundation of Yunnan Province(Grant No.2019FD071)the Yunnan Scientific Research Foundation Project(Grant 2019J0187).
文摘With the development of vehicles towards intelligence and connectivity,vehicular data is diversifying and growing dramatically.A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle(ICV)applications are proposed for the dispersed computing network composed of heterogeneous task vehicles and Network Computing Points(NCPs).Considering the amount of task data and the idle resources of NCPs,a computing resource scheduling model for NCPs is established.Taking the heterogeneous task execution delay threshold as a constraint,the optimization problem is described as the problem of maximizing the utilization of computing resources by NCPs.The proposed problem is proven to be NP-hard by using the method of reduction to a 0-1 knapsack problem.A many-to-many matching algorithm based on resource preferences is proposed.The algorithm first establishes the mutual preference lists based on the adaptability of the task requirements and the resources provided by NCPs.This enables the filtering out of un-schedulable NCPs in the initial stage of matching,reducing the solution space dimension.To solve the matching problem between ICVs and NCPs,a new manyto-many matching algorithm is proposed to obtain a unique and stable optimal matching result.The simulation results demonstrate that the proposed scheme can improve the resource utilization of NCPs by an average of 9.6%compared to the reference scheme,and the total performance can be improved by up to 15.9%.
基金the financial support from the Natural Sciences and Engineering Research Council of Canada(NSERC)。
文摘This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.
基金Supported by Beijing Nova Program of Science and Technology(Grant No.Z191100001119087)Beijing Municipal Science&Technology Commission(Grant No.Z181100004618005 and Grant No.Z18111000460000)。
文摘The electrification of vehicle helps to improve its operation efficiency and safety.Due to fast development of network,sensors,as well as computing technology,it becomes realizable to have vehicles driving autonomously.To achieve autonomous driving,several steps,including environment perception,path-planning,and dynamic control,need to be done.However,vehicles equipped with on-board sensors still have limitations in acquiring necessary environmental data for optimal driving decisions.Intelligent and connected vehicles(ICV)cloud control system(CCS)has been introduced as a new concept as it is a potentially synthetic solution for high level automated driving to improve safety and optimize traffic flow in intelligent transportation.This paper systematically investigated the concept of cloud control system from cloud related applications on ICVs,and cloud control system architecture design,as well as its core technologies development.Based on the analysis,the challenges and suggestions on cloud control system development have been addressed.
基金supported in part by the Natural Science Foundation of China under Grant 61902035 and Grant 61876023in part by the Natural Science Foundation of Shandong Province of China under Grant ZR2020LZH005in part by China Postdoctoral Science Foundation under Grant 2019M660565.
文摘Intelligent and connected vehicles have leveraged edge computing paradigm to enhance their environment comprehension and behavior planning capabilities.As the quantity of intelligent vehicles and the demand for edge computing are increasing rapidly,it becomes critical to efficiently orchestrate the communication and computation resources on edge clouds.Existing methods usually perform resource allocation in a fairly effective but still reactive manner,which is subject to the capacity of nearby edge clouds.To deal with the contradiction between the spatiotemporally varying demands for edge computing and the fixed edge cloud capacity,we proactively balance the edge computing demands across edge clouds by appropriate route planning.In this paper,route planning and resource allocation are jointly optimized to enhance intelligent driving.We propose a multi-scale decentralized optimization method to deal with the curse of dimensionality.In large-scale optimization,backpressure algorithm is used to conduct route planning and load balancing across edge clouds.In small-scale optimization,game-theoretic multi-agent learning is exploited to perform regional resource allocation.The experimental results show that the proposed algorithm outperforms the baseline algorithms which optimize route planning and resource allocation separately.
基金Project(4144081)supported by Beijing Natural Science Foundation,ChinaProjects(61403021,U1334211,61490705)supported by the National Natural Science Foundation of China+1 种基金Project(2015RC015)supported by the Fundamental Research Funds for Central Universities,ChinaProject supported by the Foundation of Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control,China
文摘The concept of connected vehicles is with great potentials for enhancing the road transportation systems in the future. To support the functions and applications under the connected vehicles frame, the estimation of dynamic states of the vehicles under the cooperative environments is a fundamental issue. By integrating multiple sensors, localization modules in OBUs(on-board units) require effective estimation solutions to cope with various operation conditions. Based on the filtering estimation framework for sensor fusion, an ensemble Kalman filter(En KF) is introduced to estimate the vehicle's state with observations from navigation satellites and neighborhood vehicles, and the original En KF solution is improved by using the cubature transformation to fulfill the requirements of the nonlinearity approximation capability, where the conventional ensemble analysis operation in En KF is modified to enhance the estimation performance without increasing the computational burden significantly. Simulation results from a nonlinear case and the cooperative vehicle localization scenario illustrate the capability of the proposed filter, which is crucial to realize the active safety of connected vehicles in future intelligent transportation.
基金supported in part by National Key R&D Program of China (No.2020YFB1807802)National Natural Science Foundation of China (Nos.61971148,U22A2054)。
文摘Digital twin is an essential enabling technology for 6G connected vehicles.Through highfidelity mobility simulation,digital twin is expected to make accurate prediction about the vehicle trajectory,and then support intelligent applications such as safety monitoring and self-driving for connected vehicles.However,it is observed that even if a digital twin model is perfectly derived,it might still fail to predict the trajectory due to tiny measurement noise or delay in the initial vehicle locations.This paper aims at investigating the sources of unpredictability of digital twin.Take the car-following behaviors in connected vehicles for case study.The theoretical analysis and experimental results indicate that the predictability of digital twin naturally depends on its system complexity.Once a system enters a complex pattern,its longterm states are unpredictable.Furthermore,our study discloses that the complexity is determined,on the one hand,by the intrinsic factors of the target physical system such as the driver’s response sensitivity and delay,and on the other hand,by the crucial parameters of the digital twin system such as the sampling interval and twining latency.
文摘Internet of things is deemed as the one of the great revolution after the age of Industrial Revolution.With the development of the communication technology,more and more entities are connected to the communication network and become one of the elements in the network.Over recent decades,in the area of intelligent transportation,pedestrian and transport infrastructure are connected to the communication network to improve the driving safety and traffic efficiency which is known as the ICV(Intelligent Connected Vehicle).This paper summarizes the global ICV progresses in the past decades and the latest activities of ICV in China,and introduces various aspects regarding the recent development of the ICV,including industry development,spectrum and standard,at the same time.
基金the National Natural Science Foundation of China(Nos.61772130 and 62072096)the Fundamental Research Funds for the Central Universities(No.2232020A-12)+1 种基金the International S&T Cooperation Program of Shanghai Science and Technology Commission(No.20220713000)the Young Top-Notch Talent Program in Shanghai。
文摘The development of intelligent connected vehicles(ICVs)has tremendously inspired the emergence of a new computing paradigm called mobile edge computing(MEC),which meets the demands of delay-sensitive on-vehicle applications.Most existing studies focusing on the issue of task offloading in ICVs assume that the MEC server can directly complete computation tasks without considering the necessity of service caching.However,this is unrealistic in practice because a large number of tasks require the use of corresponding third-party libraries and databases,that is,service caching.Therefore,we investigate the delay optimization in an MEC-enabled ICVs system with multiple mobile vehicles,resource-limited base stations(BSs),and one cloud server.We aim to determine the optimal service caching and task offloading decisions to minimize the overall system delay using mixed-integer nonlinear programming.To address this problem,we first convert it into a quadratically constrained quadratic program and then propose an efficient semidefinite relaxation-based joint service caching and task offloading(JSCTO)algorithm to obtain the service caching and task offloading decisions.In the simulations,we validate the efficiency of our proposed method by setting different numbers of vehicles and the storage capacity of BSs.The results show that our proposed JSCTO algorithm can significantly decrease the total delay of all offloaded tasks compared with the cloud processing only scheme.
基金the National Key Research and Development Program of China(2021YFB2501803)the National Natural Science Foundation of China(52172384,52002126,52102394)+2 种基金Hunan Provincial Natural Science Foundation of China(2021JJ40065)the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body(61775006)the Fundamental Research Funds for the Central Universities。
文摘As one of the typical applications of connected vehicles(CVs),the vehicle platoon control technique has been proven to have the advantages of reducing emissions,improving traffic throughout and driving safety.In this paper,a unified hierarchical framework is designed for cooperative control of CVs with both heterogeneous model parameters and structures.By separating neighboring information interaction from local dynamics control,the proposed framework is designed to contain an upper-level observing layer and a lower-level tracking control layer,which helps address the heterogeneity in vehicle parameters and structures.Within the proposed framework,an observer is designed for following vehicles to observe the leading vehicle's states using neighboring communication,while a tracking controller is designed to track the observed leading vehicle using local feedback control.Closed-loop stability in the absence and presence of communication time delay is analyzed,and the observer is further extended to a finite time convergent one to address string stability under general communication topology.Numerical simulation and field experiment verify the effectiveness of the proposed method.
基金supported by the Technology Innovation Program(10083633,Development on Big Data Analysis Technology and Business Service for Connected Vehicles)funded by the Ministry of Trade,Industry&Energy(MOTIE,Korea)。
文摘In this study,we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles.We designed two software modules:The rst to derive the Pearson correlation coefcients to analyze the collected data and the second to conduct exploratory data analysis of the collected vehicle data.In particular,we analyzed the dangerous driving patterns of motorists based on the safety standards of the Korea Transportation Safety Authority.We also analyzed seasonal fuel efciency(four seasons)and mileage of vehicles,and identied rapid acceleration,rapid deceleration,sudden stopping(harsh braking),quick starting,sudden left turn,sudden right turn and sudden U-turn driving patterns of vehicles.We implemented the density-based spatial clustering of applications with a noise algorithm for trajectory analysis based on GPS(Global Positioning System)data and designed a long shortterm memory algorithm and an auto-regressive integrated moving average model for time-series data analysis.In this paper,we mainly describe the development environment of the analysis software,the structure and data ow of the overall analysis platform,the conguration of the collected vehicle data,and the various algorithms used in the analysis.Finally,we present illustrative results of our analysis,such as dangerous driving patterns that were detected.
基金Supported by National Key R&D Program of China(Grant No.2022YFB2503203)National Natural Science Foundation of China(Grant No.U1964206).
文摘Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements.Deep reinforcement learning(DRL)exhibits excellent capability of real-time decision-making and adaptability to complex scenarios,and generalization abilities.However,it is arduous to guarantee complete driving safety and efficiency under the constraints of training samples and costs.This paper proposes a Mixture of Expert method(MoE)based on Soft Actor-Critic(SAC),where the upper-level discriminator dynamically decides whether to activate the lower-level DRL expert or the heuristic expert based on the features of the input state.To further enhance the performance of the DRL expert,a buffer zone is introduced in the reward function,preemptively applying penalties before insecure situations occur.In order to minimize collision and off-road rates,the Intelligent Driver Model(IDM)and Minimizing Overall Braking Induced by Lane changes(MOBIL)strategy are designed by heuristic experts.Finally,tested in typical simulation scenarios,MOE shows a 13.75%improvement in driving efficiency compared with the traditional DRL method with continuous action space.It ensures high safety with zero collision and zero off-road rates while maintaining high adaptability.
文摘Pavement condition monitoring and its timely maintenance is necessary to ensure the safety and quality of the roadway infrastructure. The International Roughness Index (IRI) is a commonly used measure to quantify road surface roughness and is a critical input to asset management. In Indiana, the IRI statistic contributes to roughly half of the pavement quality index computation used for asset management. Most agencies inventory IRI once a year, however, pavement conditions vary much more frequently. The objective of this paper is to develop a framework using crowdsourced connected vehicle data to identify and detect temporal changes in IRI. Over 3 billion connected vehicle records in Indiana were analyzed across 30 months between 2022 and 2024 to understand the spatiotemporal variations in roughness. Annual comparisons across all major interstates in Indiana showed the miles of interstates classified as “Good” decreased from 1896 to 1661 miles between 2022 and 2024. The miles of interstate classified as “Needs Maintenance” increased from 82 to 120 miles. A detailed case study showing monthly and daily changes of estimated IRI on I-65 are presented along with supporting dashcam images. Although the crowdsourced IRI estimates are not as robust as traditional specialized pavement profilers, they can be obtained on a monthly, weekly, or even daily basis. The paper concludes by suggesting a combination of frequent crowdsourced IRI and commercially available dashcam imagery of roadway can provide an agile and responsive mechanism for agencies to implement pavement asset management programs that can complement existing annual programs.
基金jointly supported by the National Key Research and Development Program of China(No.2022ZD0115600)National Natural Science Foundation of China(No.52072067)+3 种基金Natural Science Foundation of Jiangsu Province(No.BK20210249)China Postdoctoral Science Foundation(No.2020M681466)Jiangsu Planned Projects for Postdoctoral Research Funds(No.SBK2021041144)Jiangsu Planned Projects for Postdoctoral Research Funds(No.2021K094A)。
文摘Connected automated vehicles(CAVs)rely heavily on intelligent algorithms and remote sensors.If the control center or on-board sensors are under cyber-attack due to the security vulnerability of wireless communication,it can cause significant damage to CAVs or passengers.The primary objective of this study is to model cyberattacked traffic flow and evaluate the impacts of cyber-attack on the traffic system filled with CAVs in a connected environment.Based on the analysis on environmental perception system and possible cyber-attacks on sensors,a novel lane-changing model for CAVs is proposed and multiple traffic scenarios for cyber-attacks are designed.The impact of the proportion of cyber-attacked vehicles and the severity of the cyber-attack on the lanechanging process is then quantitatively analyzed.The evaluation indexes include spatio-temporal evolution of average speed,spatial distribution of selected lane-changing gaps,lane-changing rate distribution,lane-changing preparation search time,efficiency and safety.Finally,the numerical simulation results show that the freeway traffic near an off-ramp is more sensitive to the proportion of cyber-attacked vehicles than to the severity of the cyber-attack.Also,when the traffic system is under cyber-attack,more unsafe back gaps are chosen for lane-changing,especially in the center lane.Therefore,more lane-changing maneuvers are concentrated on approaching the off-ramp,causing severe congestions and potential rear-end collisions.In addition,as the number of cyber-attacked vehicles and the severity of cyber-attacks increase,the road capacity and safety level will rapidly decrease.The results of this study can provide a theoretical basis for accident avoidance and efficiency improvement for the design of CAVs and management of automated highway systems.
基金supported by Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems(Changsha University of Science&Technology)(Grant No.kfj220703).
文摘Traffic flow mobility on expressway plays an important role in urban development.With the emergent technologies,connected vehicles,including both connected automated vehicles(CAVs)and connected regular vehicles(CRVs),are equipped with connectivity features to improve efficiency of urban expressway in a mixed traffic scenario.Existing research indicates that without targeted management,the integration of CAVs and CRVs into regular vehicles(RVs)traffic can lead to a series of congestion issues.A potential solution lies in the implementation of dedicated lanes,each designated for specific vehicle types,which could alleviate traffic flow complications.Therefore,this paper proposes a dynamic optimal lane management strategy for multi-lane mixed traffic urban expressway,aimed at maximizing the whole discharge flow.To begin with,we present an analytical method to mathematically derive discharge flow of each lane type including mixed traffic lane,CAV/CRV dedicated lane,and RV dedicated lane,from the perspective of both traffic demand and traffic capacity.Then,taking a three-lane mixed traffic urban expressway as an application,we conduct a numerical analysis of the dynamic optimal lane management strategy,based on comparisons among eight distinct strategies,under varying factors of traffic demands,penetration rates of CAVs and CRVs,and allocation proportions of vehicles upstream assigned to mixed traffic lanes.The results validate the effectiveness of the analytical method proposed for lane management strategies.It also indicates that the aforementioned system factors have significant impacts on the dynamic optimal lane management strategy.
文摘With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.
基金supported by the Program of Humanities and Social Science of the Ministry of Education of China(No.24YJA630013)the Natural Science Foundation of Ningbo of China(No.2024J125)the“Innovation Yongjiang 2035”Key R&D Programme(No.2024H032),China。
文摘With the advancement of connected vehicle(CV)technology,an increasing number of CVs will appear on urban roads.Data collected by CVs can be used to optimize signal parameters at intersections,thus improving traffic efficiency.In this study,we design a real-time adaptive signal control method for an arterial road with multiple intersections with low penetration rates.By utilizing vehicle arrival information collected by CVs,our method rapidly determines optimal signal phasing and timing(SPaT).The proposed adaptive signal control method was tested with the Simulation of Urban Mobility(SUMO)software,and was found to reduce total travel delay in the network better than a fixed coordination control method.The performance of the proposed method in reducing travel delay is expected to improve as CV detection range increases.
基金supported by National Hi-Tech Research and Development Program of China(Grant Nos.2015BAG17B04&2013BAG08B01)U.S.National Science Foundation(Grant No.1544910)U.S.Department of Energy GATE Program and China Scholarship Council
文摘This paper presents a decentralized fuel efficient model predictive control(MPC) strategy for a group of connected vehicles incorporating vertical vibration. To capture the vehicle vibration dynamics, the dynamics of the suspension system is integrated with the longitudinal dynamics of the vehicle. Furthermore, a MPC framework with finite time horizon is formulated to calculate the optimal velocity profile that compromises fuel economy, mobility and ride comfort for every individual vehicle with the safety and physical constraints considered. In the MPC framework, the target velocity is calculated using signal phase and timing(SPAT)information to reduce the number of stoppage at red lights, and the vertical acceleration is calculated parallel to the calculation of the fuel consumption. The MPC optimal problem is solved with fast-MPC approach which enhances the computational efficiency via exploiting the structure of the control system and approximate methods. Simulation studies are conducted over different SPATs and connectivity penetration rates and the results validate the advantages of the proposed control architecture.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51905419 and 51175419)。
文摘Advanced vehicular control technologies rely on accurate speed prediction to make ecological and safe decisions. This paper proposes a novel stochastic speed prediction method for connected vehicles by incorporating a Bayesian network(BN) and a Back Propagation(BP) neural network. A BN model is first designed for predicting the stochastic vehicular speed in a priori. To improve the accuracy of the BN-based speed prediction, a BP-based predicted speed error compensation module is constructed by formulating a mapping between the predicted speed and its corresponding prediction error. In the end, a filtering algorithm is developed to smoothen the compensated stochastic vehicular speed. To validate the workings of the proposed approaches in experiments, two typical scenarios are considered: one predecessor vehicle in a double-vehicle scenario and two predecessor vehicles in a multi-vehicle scenario. Simulation results under the considered scenarios demonstrate that the proposed BN-BP fusion method outperforms the BN-based method with respect to the root mean square error, standardized residuals, and R-squared, and the online prediction time of proposed fusion prediction can satisfy a real-time application requirement. The main highlighted contributions of this article are threefold:(1) We put forward an improved BN method, which is combined with a BP neural network, to construct a stochastic vehicular speed prediction method under connected driving;(2) different from existing methods, a unique interconnected framework that consists of a stochastic vehicular speed prediction module, a compensation module, and a speed smoothing module is proposed;(3) extensive simulation studies based on a set of evaluation metrics are illustrated to reveal the advantages and merits of the proposed approaches.
