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
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
The connectivity of shale pores and the occurrence of movable oil in shales have long been the focus of research.In this study,samples from wells BX7 and BYY2 in the Eq3^4-10 cyclothem of Qianjiang Formation in the Qi...The connectivity of shale pores and the occurrence of movable oil in shales have long been the focus of research.In this study,samples from wells BX7 and BYY2 in the Eq3^4-10 cyclothem of Qianjiang Formation in the Qianjiang depression,were analyzed.A double mercury injection method was used to distinguish between invalid and effective connected pores.The pore characteristics for occurrence of retained hydrocarbons and movable shale oil were identified by comparing pore changes in low temperature nitrogen adsorption and high pressure mercury injection experiments before and after extraction and the change in the mercury injection amounts in the pores between two separate mercury injections.The results show that less than 50%of the total connected pores in the Eq34-10 cyclothem samples are effective.The development of effective connected pores affects the mobility of shale oil but varies with different lithofacies.The main factor limiting shale oil mobility in Well BX7 is the presence of pores with throat sizes less than 15 nm.In Well BYY2,residual mercury in injection testing of lamellar dolomitic mudstone facies was mainly concentrated in pores with throats of 10-200 nm,and in bulk argillaceous dolomite facies,it was mainly concentrated at 60-300 nm.The throats of hydrocarbon-retaining pores can be 5 nm or even smaller,but pores with movable shale oil in the well were found to have throat sizes greater than 40 nm.Excluding the influence of differences in wettability,the movability of shale oil is mainly affected by differences in lithofacies,the degree of pore deformation caused by diagenesis,the complexity of pore structures,and the connectivity of pore throats.Dissolution and reprecipitation of halite also inhibit the mobility of shale oil.展开更多
Using satellites to complete spectrum monitoring tasks can effectively receive and process electromagnetic spectrum signals emitted by radiation sources.However,due to the shortage of satellite storage,computing and n...Using satellites to complete spectrum monitoring tasks can effectively receive and process electromagnetic spectrum signals emitted by radiation sources.However,due to the shortage of satellite storage,computing and network resources,the intersatellite coordination is weak,and with the massive growth of spectrum data,the traditional cloud computing mode cannot meet the requirements of electromagnetic spectrum monitoring in terms of real-time,bandwidth,and security.We apply edge computing technology and deep learning technology to the satellite.Aiming at the problems of distributed satellite management and control,we propose a space-based distributed electromagnetic spectrum monitoring intelligent connected cloud-edge collaborative architecture SpaceEdge.SpaceEdge applies edge computing and artificial intelligence technology to space-based spectrum monitoring.SpaceEdge deploys intelligent monitoring algorithms to edge nodes to form edge intelligent satellite,and uses the cloud to uniformly manage and control heterogeneous edge satellite and monitor satellite resources.In addition,SpaceEdge can also adjust edge intelligent spectrum monitoring applications as needed to achieve effective coordination of inter-satellite algorithms and data to achieve the purpose of collaborative monitoring.Finally,SpaceEdge was experimentally verified,and the results proved the feasibility of SpaceEdge and can improve the timeliness and autonomy of the distributed satellite’s coordinated signal monitoring.展开更多
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 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
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
OBJECTIVE:To explore the relationship between colorectal polyps and pulmonary nodules from the perspective of the lung and the large intestine being internally and externally connected,aiming to provide a theoretical ...OBJECTIVE:To explore the relationship between colorectal polyps and pulmonary nodules from the perspective of the lung and the large intestine being internally and externally connected,aiming to provide a theoretical basis for clinical diagnosis and treatment.METHODS:We retrospectively analyzed the data of patients who underwent electronic colonoscopy and were found to have colorectal polyps at the Gastrointestinal Endoscopy Center of Dongfang Hospital,Beijing University of Chinese Medicine,from January 1,2017,to December 31,2023.We also reviewed their lung CT results and used statistical software to analyze the recurrence,location,size,and pathology of colorectal polyps in relation to the presence,number,and size of pulmonary nodules.RESULTS:Both colorectal polyps and pulmonary nodules are more common in elderly males.Patients with recurrent colorectal polyps are more likely to have pulmonary nodules,which tend to be located in the left colon and are more likely to be adenomatous in nature;those without pulmonary nodules show no clear pattern in polyp distribution,with a tendency towards inflammatory and hyperplastic pathology;the data from this study suggests that the proportion of lung nodules larger than 0.5 cm in the recurrent group is higher than in the non-recurrent group,and the proportion of colorectal polyps larger than 1 cm in the recurrent group is also higher than in the non-recurrent group.CONCLUSION:There is a certain connection between the pathogenesis and treatment of colorectal polyps and pulmonary nodules.Cold,phlegm,dampness,blood stasis,and toxic coagulation are common pathogenic factors of the two diseases.Patients with larger colorectal polyps should be advised to undergo regular colonoscopy.Patients with recurrent polyps or those with left colon necrosis or cancer indicated by colonoscopy should be advised to complete lung related examinations to rule out the possibility of pulmonary nodules.展开更多
Infrastructure and energy are two important areas for African countries to achieve sustainable development,as well as are among the priorities in the African Union’s Agenda 2063,the continent’s ambitious development...Infrastructure and energy are two important areas for African countries to achieve sustainable development,as well as are among the priorities in the African Union’s Agenda 2063,the continent’s ambitious development blueprint.In February,Lerato Mataboge was elected as the African Union Commissioner for Infrastructure and Energy.She is a global policy and trade and investment facilitation expert and was the deputy director general in the South African Department of Trade,Industry and Competition when she was elected.展开更多
Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current ...Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.展开更多
To improve the fault diagnosis accuracy of a PV grid-connected inverter,a PV grid-connected inverter data diagnosis method based on MPA-VMD-PSO-BiLSTM is proposed.Firstly,unlike the traditional VMD algorithm which rel...To improve the fault diagnosis accuracy of a PV grid-connected inverter,a PV grid-connected inverter data diagnosis method based on MPA-VMD-PSO-BiLSTM is proposed.Firstly,unlike the traditional VMD algorithm which relies on manual experience to set parameters(e.g.,noise tolerance,penalty parameter,number of decompositions),this paper achieves adaptive optimization of parameters through MPA algorithmto avoid the problemof feature information loss caused by manual parameter tuning,and adopts the improved VMD algorithm for feature extraction of DC-side voltage data signals of PV-grid-connected inverters;and then,adopts the PSO algorithm for theThen,the PSO algorithm is used to optimize the optimal batch size,the number of nodes in the hidden layer and the learning rate of the BiLSTM network,which significantly improves the model’s ability to capture the long-term dependent features of the PV inverter’s timing signals,to construct the PV grid-connected inverter prediction model of PSO-BiLSTM,and predict the capacitance value of the PVgrid-connected inverter.Finally,diagnostic experiments are carried out based on the expected capacitance value and the capacitance failure criterion.The results showthat compared with the traditional VMD algorithm,the MPA-optimised VMD improves the signal-to-noise ratio(SNR)of the signal decomposition from 28.5 to 33.2 dB(16.5%improvement).After combining with the PSO-BiLSTM model,the mean absolute percentage error(MAPE)of the fault diagnosis is reduced to 1.31%,and the coefficient of determination(R2)is up to 0.99.It is concluded that the present method has excellent diagnostic performance of PV grid-connected inverter data signals and effectively improves the accuracy of PV grid-connected inverter diagnosis.展开更多
This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional(Bitcoin and Ethe...This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional(Bitcoin and Ethereum)and Islamic(Stellar and Ripple)cryptocurrencies.Using a novel time-varying parameter vector autoregression(TVP-VAR)asymmetric connectedness approach combined with a high frequency(hourly)dataset ranging from 1st June 2018 to 22nd July 2022,we find that(i)good and bad spillovers are time-varying;(ii)bad volatility spillovers are more pronounced than good spillovers;(iii)a strong asymmetry in the volatility spillovers exists in the cryptocurrency market;and(iv)conventional cryptocurrencies dominate Islamic cryptocurrencies.Specifically,Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.展开更多
This study examines the time-varying asymmetric interlinkages between nine US sectoral returns from January 2020 to January 2023.To this end,we used the time-varying parameter vector autoregression(TVP-VAR)asymmetric ...This study examines the time-varying asymmetric interlinkages between nine US sectoral returns from January 2020 to January 2023.To this end,we used the time-varying parameter vector autoregression(TVP-VAR)asymmetric connectedness approach of Adekoya et al.(Resour Policy 77:102728,2022a,Resour Policy 78:102877,2022b)and analyzed the time-varying transmitting/receiving roles of sectors,considering the positive and negative impacts of the spillovers.We further estimate negative spillovers networks at two burst times(the declaration of the COVID-19 pandemic by the World Health Organization on 11 March 2020 and the start of Russian-Ukrainian war on 24 February 2022,respectively).Moreover,we performed a portfolio back-testing analysis to determine the time-varying portfolio allocations and hedging the effectiveness of different portfolio construction techniques.Our results reveal that(i)the sectoral return series are strongly interconnected,and negative spillovers dominate the study period;(ii)US sectoral returns are more sensitive to negative shocks,particularly during the burst times;(iii)the overall,positive,and negative connectedness indices reached their maximums on March 16,2020;(iv)the industry sector is the largest transmitter/recipient of return shocks on average;and(v)the minimum correlation and connectedness portfolio approaches robustly capture asymmetries.Our findings provide suggestions for investors,portfolio managers,and policymakers regarding optimal portfolio strategies and risk supervision.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
Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate resul...Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.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
Dear Editor,This letter concerns the development of approximately bi-similar symbolic models for a discrete-time interconnected switched system(DT-ISS).The DT-ISS under consideration is formed by connecting multiple s...Dear Editor,This letter concerns the development of approximately bi-similar symbolic models for a discrete-time interconnected switched system(DT-ISS).The DT-ISS under consideration is formed by connecting multiple switched systems known as component switched systems(CSSs).Although the problem of constructing approximately bi-similar symbolic models for DT-ISS has been addressed in some literature,the previous works have relied on the assumption that all the subsystems of CSSs are incrementally input-state stable.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
An intuitive portrayal of the correlation between the carbon and energy markets is essential for risk control and green financial investment management.In this paper,we investigate the asymmetric spillovers between th...An intuitive portrayal of the correlation between the carbon and energy markets is essential for risk control and green financial investment management.In this paper,we investigate the asymmetric spillovers between the carbon mar-ket and energy market returns.To achieve that,we improve the Diebold-Yilmaz index model by a time-varying vector autoregressive(TVP-VAR)model.In a unified network,our daily dataset includes the closing prices of the Hubei carbon market,Shenzhen carbon market,coal futures,and energy stock index.The findings reveal that both the Hubei and Shen-zhen pilots typically generate net information spillovers on energy futures.In connection with energy stocks,the Hubei carbon market acts as a net receiver,while the Shenzhen carbon market is a net transmitter.Compared with the Hubei pi-lot,the Shenzhen pilot is more tightly connected to the energy markets.Furthermore,the spillovers of the carbon markets exhibit significant asymmetry.In most cases,they have more substantial impacts on the energy markets when the prices of emission allowances rise.The direction and magnitude of asymmetric spillovers across markets vary over time and can be influenced by certain economic or political events.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
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.
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supported by the National Natural Science Foundation of China(No.U19B6003)。
文摘The connectivity of shale pores and the occurrence of movable oil in shales have long been the focus of research.In this study,samples from wells BX7 and BYY2 in the Eq3^4-10 cyclothem of Qianjiang Formation in the Qianjiang depression,were analyzed.A double mercury injection method was used to distinguish between invalid and effective connected pores.The pore characteristics for occurrence of retained hydrocarbons and movable shale oil were identified by comparing pore changes in low temperature nitrogen adsorption and high pressure mercury injection experiments before and after extraction and the change in the mercury injection amounts in the pores between two separate mercury injections.The results show that less than 50%of the total connected pores in the Eq34-10 cyclothem samples are effective.The development of effective connected pores affects the mobility of shale oil but varies with different lithofacies.The main factor limiting shale oil mobility in Well BX7 is the presence of pores with throat sizes less than 15 nm.In Well BYY2,residual mercury in injection testing of lamellar dolomitic mudstone facies was mainly concentrated in pores with throats of 10-200 nm,and in bulk argillaceous dolomite facies,it was mainly concentrated at 60-300 nm.The throats of hydrocarbon-retaining pores can be 5 nm or even smaller,but pores with movable shale oil in the well were found to have throat sizes greater than 40 nm.Excluding the influence of differences in wettability,the movability of shale oil is mainly affected by differences in lithofacies,the degree of pore deformation caused by diagenesis,the complexity of pore structures,and the connectivity of pore throats.Dissolution and reprecipitation of halite also inhibit the mobility of shale oil.
文摘Using satellites to complete spectrum monitoring tasks can effectively receive and process electromagnetic spectrum signals emitted by radiation sources.However,due to the shortage of satellite storage,computing and network resources,the intersatellite coordination is weak,and with the massive growth of spectrum data,the traditional cloud computing mode cannot meet the requirements of electromagnetic spectrum monitoring in terms of real-time,bandwidth,and security.We apply edge computing technology and deep learning technology to the satellite.Aiming at the problems of distributed satellite management and control,we propose a space-based distributed electromagnetic spectrum monitoring intelligent connected cloud-edge collaborative architecture SpaceEdge.SpaceEdge applies edge computing and artificial intelligence technology to space-based spectrum monitoring.SpaceEdge deploys intelligent monitoring algorithms to edge nodes to form edge intelligent satellite,and uses the cloud to uniformly manage and control heterogeneous edge satellite and monitor satellite resources.In addition,SpaceEdge can also adjust edge intelligent spectrum monitoring applications as needed to achieve effective coordination of inter-satellite algorithms and data to achieve the purpose of collaborative monitoring.Finally,SpaceEdge was experimentally verified,and the results proved the feasibility of SpaceEdge and can improve the timeliness and autonomy of the distributed satellite’s coordinated signal monitoring.
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 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.
基金Beijing Natural Science Foundation:Research on the Mechanism of Chaiqi Decoction in Improving Vascular Endothelial Injury in Metabolic Syndrome by Regulating Pyroptosis via miR-21(No.7212181)。
文摘OBJECTIVE:To explore the relationship between colorectal polyps and pulmonary nodules from the perspective of the lung and the large intestine being internally and externally connected,aiming to provide a theoretical basis for clinical diagnosis and treatment.METHODS:We retrospectively analyzed the data of patients who underwent electronic colonoscopy and were found to have colorectal polyps at the Gastrointestinal Endoscopy Center of Dongfang Hospital,Beijing University of Chinese Medicine,from January 1,2017,to December 31,2023.We also reviewed their lung CT results and used statistical software to analyze the recurrence,location,size,and pathology of colorectal polyps in relation to the presence,number,and size of pulmonary nodules.RESULTS:Both colorectal polyps and pulmonary nodules are more common in elderly males.Patients with recurrent colorectal polyps are more likely to have pulmonary nodules,which tend to be located in the left colon and are more likely to be adenomatous in nature;those without pulmonary nodules show no clear pattern in polyp distribution,with a tendency towards inflammatory and hyperplastic pathology;the data from this study suggests that the proportion of lung nodules larger than 0.5 cm in the recurrent group is higher than in the non-recurrent group,and the proportion of colorectal polyps larger than 1 cm in the recurrent group is also higher than in the non-recurrent group.CONCLUSION:There is a certain connection between the pathogenesis and treatment of colorectal polyps and pulmonary nodules.Cold,phlegm,dampness,blood stasis,and toxic coagulation are common pathogenic factors of the two diseases.Patients with larger colorectal polyps should be advised to undergo regular colonoscopy.Patients with recurrent polyps or those with left colon necrosis or cancer indicated by colonoscopy should be advised to complete lung related examinations to rule out the possibility of pulmonary nodules.
文摘Infrastructure and energy are two important areas for African countries to achieve sustainable development,as well as are among the priorities in the African Union’s Agenda 2063,the continent’s ambitious development blueprint.In February,Lerato Mataboge was elected as the African Union Commissioner for Infrastructure and Energy.She is a global policy and trade and investment facilitation expert and was the deputy director general in the South African Department of Trade,Industry and Competition when she was elected.
基金supported in part by the National Natural Science Foundation of China under Grant 62303090,U2330206in part by the Postdoctoral Science Foundation of China under Grant 2023M740516+1 种基金in part by the Natural Science Foundation of Sichuan Province under Grant 2024NSFSC1480in part by the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.
基金supported by Science and Technology Projects of Jiangsu Province(No.BE2022003)Science and Technology Projects of Jiangsu Province(No.BE2022003-5).
文摘To improve the fault diagnosis accuracy of a PV grid-connected inverter,a PV grid-connected inverter data diagnosis method based on MPA-VMD-PSO-BiLSTM is proposed.Firstly,unlike the traditional VMD algorithm which relies on manual experience to set parameters(e.g.,noise tolerance,penalty parameter,number of decompositions),this paper achieves adaptive optimization of parameters through MPA algorithmto avoid the problemof feature information loss caused by manual parameter tuning,and adopts the improved VMD algorithm for feature extraction of DC-side voltage data signals of PV-grid-connected inverters;and then,adopts the PSO algorithm for theThen,the PSO algorithm is used to optimize the optimal batch size,the number of nodes in the hidden layer and the learning rate of the BiLSTM network,which significantly improves the model’s ability to capture the long-term dependent features of the PV inverter’s timing signals,to construct the PV grid-connected inverter prediction model of PSO-BiLSTM,and predict the capacitance value of the PVgrid-connected inverter.Finally,diagnostic experiments are carried out based on the expected capacitance value and the capacitance failure criterion.The results showthat compared with the traditional VMD algorithm,the MPA-optimised VMD improves the signal-to-noise ratio(SNR)of the signal decomposition from 28.5 to 33.2 dB(16.5%improvement).After combining with the PSO-BiLSTM model,the mean absolute percentage error(MAPE)of the fault diagnosis is reduced to 1.31%,and the coefficient of determination(R2)is up to 0.99.It is concluded that the present method has excellent diagnostic performance of PV grid-connected inverter data signals and effectively improves the accuracy of PV grid-connected inverter diagnosis.
文摘This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional(Bitcoin and Ethereum)and Islamic(Stellar and Ripple)cryptocurrencies.Using a novel time-varying parameter vector autoregression(TVP-VAR)asymmetric connectedness approach combined with a high frequency(hourly)dataset ranging from 1st June 2018 to 22nd July 2022,we find that(i)good and bad spillovers are time-varying;(ii)bad volatility spillovers are more pronounced than good spillovers;(iii)a strong asymmetry in the volatility spillovers exists in the cryptocurrency market;and(iv)conventional cryptocurrencies dominate Islamic cryptocurrencies.Specifically,Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.
文摘This study examines the time-varying asymmetric interlinkages between nine US sectoral returns from January 2020 to January 2023.To this end,we used the time-varying parameter vector autoregression(TVP-VAR)asymmetric connectedness approach of Adekoya et al.(Resour Policy 77:102728,2022a,Resour Policy 78:102877,2022b)and analyzed the time-varying transmitting/receiving roles of sectors,considering the positive and negative impacts of the spillovers.We further estimate negative spillovers networks at two burst times(the declaration of the COVID-19 pandemic by the World Health Organization on 11 March 2020 and the start of Russian-Ukrainian war on 24 February 2022,respectively).Moreover,we performed a portfolio back-testing analysis to determine the time-varying portfolio allocations and hedging the effectiveness of different portfolio construction techniques.Our results reveal that(i)the sectoral return series are strongly interconnected,and negative spillovers dominate the study period;(ii)US sectoral returns are more sensitive to negative shocks,particularly during the burst times;(iii)the overall,positive,and negative connectedness indices reached their maximums on March 16,2020;(iv)the industry sector is the largest transmitter/recipient of return shocks on average;and(v)the minimum correlation and connectedness portfolio approaches robustly capture asymmetries.Our findings provide suggestions for investors,portfolio managers,and policymakers regarding optimal portfolio strategies and risk supervision.
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This research is partially supported by grant from the National Natural Science Foundation of China(No.72071019)grant from the Natural Science Foundation of Chongqing(No.cstc2021jcyj-msxmX0185)grant from the Chongqing Graduate Education and Teaching Reform Research Project(No.yjg193096).
文摘Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.
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 Natural Science Foundation of Shanghai Municipality(21ZR1423400)the National Natural Science Funds of China(62173217)NSFC/Royal Society Cooperation and Exchange Project(62111530154,IEC\NSFC\201107).
文摘Dear Editor,This letter concerns the development of approximately bi-similar symbolic models for a discrete-time interconnected switched system(DT-ISS).The DT-ISS under consideration is formed by connecting multiple switched systems known as component switched systems(CSSs).Although the problem of constructing approximately bi-similar symbolic models for DT-ISS has been addressed in some literature,the previous works have relied on the assumption that all the subsystems of CSSs are incrementally input-state stable.
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supported by the National Natural Science Foundation of China(71973001).
文摘An intuitive portrayal of the correlation between the carbon and energy markets is essential for risk control and green financial investment management.In this paper,we investigate the asymmetric spillovers between the carbon mar-ket and energy market returns.To achieve that,we improve the Diebold-Yilmaz index model by a time-varying vector autoregressive(TVP-VAR)model.In a unified network,our daily dataset includes the closing prices of the Hubei carbon market,Shenzhen carbon market,coal futures,and energy stock index.The findings reveal that both the Hubei and Shen-zhen pilots typically generate net information spillovers on energy futures.In connection with energy stocks,the Hubei carbon market acts as a net receiver,while the Shenzhen carbon market is a net transmitter.Compared with the Hubei pi-lot,the Shenzhen pilot is more tightly connected to the energy markets.Furthermore,the spillovers of the carbon markets exhibit significant asymmetry.In most cases,they have more substantial impacts on the energy markets when the prices of emission allowances rise.The direction and magnitude of asymmetric spillovers across markets vary over time and can be influenced by certain economic or political events.
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.
