Dear Editor,This letter studies the motion planning issue for an autonomous underwater vehicle(AUV)in obstacle environment.We propose a novel integrated detection-communication waveform that enables simultaneous obsta...Dear Editor,This letter studies the motion planning issue for an autonomous underwater vehicle(AUV)in obstacle environment.We propose a novel integrated detection-communication waveform that enables simultaneous obstacle detection and self-localization.展开更多
The intelligent transportation systems require secure,low-latency,and reliable communication architectures to enable the real-time vehicular application.This paper proposes an edge-intelligent semantic aggregation(EIS...The intelligent transportation systems require secure,low-latency,and reliable communication architectures to enable the real-time vehicular application.This paper proposes an edge-intelligent semantic aggregation(EISA)framework for 6G unmanned aerial vehicle(UAV)-assisted Internet of vehicles(IoV)networks that integrates task-driven semantic communication,deep reinforcement learning(DRL)-based edge intelligence,and blockchain-based semantic validation across 6G terahertz(THz)links.UAVs in the proposed architecture serve as adaptive edge nodes that receive semantically vital information about the vehicle at any given stage,optimize aggregation and transmission parameters dynamically,and guarantee data integrity through a structured,lightweight consortium blockchain that signs semantically detailed representations rather than raw packets.Simulation results from a hybrid NS-3,MATLAB,and Python environment indicate that the proposed framework can achieve up to 45%reduction in end-to-end latency,an approximately 70%increase in throughput,and semantic efficiency with blockchain verification delays of less than 20 ms(more than 98%).These findings support the effectiveness of the proposed co-design for achieving context-aware,energy-efficient,and reliable communication under heavy-traffic conditions.The proposed framework provides a flexible and scalable foundation for next-generation 6G-enabled automotive networks,with subsequent growth toward federated learning-based collaborative intelligence,digital-twinassisted traffic modeling,and quantum-safe blockchain mechanisms to enhance scalability,intelligence,and long-term security.展开更多
Ammonia(NH3)has been widely recognized as a key precursor of atmospheric secondary aerosol formation.Vehicle emission is a major source of urban atmospheric NH3.With the tightening of emission standards and the growin...Ammonia(NH3)has been widely recognized as a key precursor of atmospheric secondary aerosol formation.Vehicle emission is a major source of urban atmospheric NH3.With the tightening of emission standards and the growing trend of vehicle fleet electrification,it is imperative to update the emission factors for NH3 from real-world on-road fleets.In this study,a tunnel measurement was conducted in the urban area of Tianjin,China.The fleet-average NH3 emission factor(EF)was 11.2 mg/(km·veh),significantly lower than those in previous studies,showing the benefit of emission standard updating.Through a multiple linear regression analysis,the EFs of light-duty gasoline vehicles,light-duty diesel vehicles,and heavy-duty diesel vehicles(HDDVs)were estimated to be 5.7±0.6 mg/(km·veh),40.8±5.1 mg/(km·veh),and 160.2±16.6 mg/(km·veh),respectively.Based on the results from this study,we found that HDDVs,which comprise<3%of the total vehicles may contribute approximately 22%of total NH3 emissions in Tianjin.Our results highlight NH3 emissions from HDDVs,a previously potentially overlooked source of NH3 emissions in urban areas.The actual on-road NH3 emissions from HDDVs may exceed current expectations,posing a growing concern for the future.展开更多
The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and...The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.展开更多
Dear Editor,This letter addresses the formation control problem for unmanned surface vehicles(USVs)under GPS-denied environments.A novel visual servo formation control scheme,utilizing a monocular camera on the follow...Dear Editor,This letter addresses the formation control problem for unmanned surface vehicles(USVs)under GPS-denied environments.A novel visual servo formation control scheme,utilizing a monocular camera on the follower to obtain the leader’s global position,is developed,which is also capable of guaranteeing collision avoidance and visibility maintenance(CA&VM)raised by the requirement of actual formation navigation.展开更多
Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods a...Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.展开更多
To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the conc...To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.展开更多
According to the China Association of Automobile Manufacturers (CAAM),China's auto industry reached record highs in 2025,with production and sales at 34.53 million and 34.4 million vehicles,respectively.This secur...According to the China Association of Automobile Manufacturers (CAAM),China's auto industry reached record highs in 2025,with production and sales at 34.53 million and 34.4 million vehicles,respectively.This secured China's position as the world's largest auto market for the 17th year in a row.展开更多
Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering.This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies,aiming to develop a f...Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering.This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies,aiming to develop a fatigue life prediction method for critical bogie components using deep learning models and measured track load spectra.Extensive field tests on Chengdu Metro Line 1 were conducted to acquire acceleration and stress response data of the wheelset lifting lug,generating training samples for the neural network system.Component stress responses were calculated via time-domain track acceleration and validated against in-situ stress measurements.Results show that neural network-fitted dynamic stress values exhibit excellent consistency with measured data,with errors constrained within 5%.This study validates the proposed small-sample deep learning approach as an effective and accurate solution for fatigue life prediction of critical bogie components under operational load conditions.展开更多
Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees l...Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.展开更多
The radiative heat flux of the plume from reusable rockets is a critical parameter during the launch and return processes.This paper proposes a method for calculating radiative heat flux with higher accuracy than prev...The radiative heat flux of the plume from reusable rockets is a critical parameter during the launch and return processes.This paper proposes a method for calculating radiative heat flux with higher accuracy than previously reported for a recoverable nine-engine liquid-propellant rocket.Based on the Radiative Transfer Equation(RTE),this study employs the discrete transfer method to solve the transient RTE problem using physical properties to describe the problem while avoiding the need to directly solve mathematical equations.The proposed method can effectively determine the radiative heat flux of the flow field and is applicable to problems involving various geometries.Calculations reveal that during the ascent phase of the rocket,the radiative heat flux at the base of the vehicle reaches its maximum in the initial stages of the lift-off,reaching a maximum of~50 kW/m^(2),which is 2.24 times the maximum value during the return phase.During the deceleration stage of re-entry into the atmosphere,the maximum radiative heat flux recorded on the sidewall of the rocket is 29.1 kW/m^(2);the maximum heat flux on the bottom surface is approximately 22.3 kW/m^(2),accounting for 76.6%of that on the rocket's sidewall.This provides a basis for the thermal protection design of the rocket's bottom and walls as well as for the thermal management of cryogenic propellant tanks.Future research will involve ground engine testing and flight experiments to further validate the proposed model.展开更多
To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy s...To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates.展开更多
Autonomous vehicles operate without direct human intervention,which introduces safety risks that differ from those of conventional vehicles.Although many studies have examined safety issues related to autonomous drivi...Autonomous vehicles operate without direct human intervention,which introduces safety risks that differ from those of conventional vehicles.Although many studies have examined safety issues related to autonomous driving,high-risk situations have often been defined using single indicators,making it difficult to capture the complex and evolving nature of accident risk.To address this limitation,this study proposes a structured framework for defining and analyzing high-risk situations throughout the traffic accident process.High-risk situations are described using three complementary indicators:accident likelihood,accident severity,and accident duration.These indicators explain how risk emerges,increases,and persists over time.Based on this concept,a framework for traffic accident visualization analysis is developed to support phase-specific risk assessment and visualization.The framework combines accident-phase information with factor-level risk contributions,allowing systematic identification of key factors and their interactions across different accident stages.Using combinations of the three indicators,high-risk situations are classified into twenty-seven distinct types,providing a clear typology for complex accident scenarios involving autonomous vehicles.The applicability of the proposed framework is demonstrated through two representative accident scenarioswith different risk characteristics.The results showthat the framework effectively captures interactions among multiple risk factors,explains how risk levels change from pre-crash to post-crash phases,and identifies contributing factors that are difficult to detect using conventional traffic accident investigation methods.Overall,the proposed framework offers a practical basis for autonomous vehicle accident analysis,safety evaluation,and policy-related decision-making.展开更多
With the increasing complexity of logistics operations,traditional static vehicle routing models are no longer sufficient.In practice,customer demands often arise dynamically,and multi-depot systems are commonly used ...With the increasing complexity of logistics operations,traditional static vehicle routing models are no longer sufficient.In practice,customer demands often arise dynamically,and multi-depot systems are commonly used to improve efficiency.This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand.New customers appear in the delivery process at any time and are periodically optimized according to time slices.Then,we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem.The classical ant colony algorithm uses spatial distance to select nodes,while TS-ACO considers the impact of both temporal and spatial distance on node selection.Meanwhile,we adopt Cordeau’s Multi-Depot Vehicle Routing Problem with Time Windows(MDVRPTW)dataset to evaluate the performance of our system.According to the experimental results,TS-ACO,which considers spatial and temporal distance,is more effective than the classical ACO,which only considers spatial distance.展开更多
Unmanned aerial vehicles(UAVs)face the challenge of autonomous obstacle avoidance in complex,multi-obstacle environments.Behavior cloning offers a promising approach to rapidly acquire a learning policy from limited e...Unmanned aerial vehicles(UAVs)face the challenge of autonomous obstacle avoidance in complex,multi-obstacle environments.Behavior cloning offers a promising approach to rapidly acquire a learning policy from limited expert demonstrations.However,pure imitation learning inherently suffers from poor exploration and limited generalization,typically necessitating extensive datasets to train competent student policies.We utilize a cross-modal variational autoencoder(CM-VAE)to extract compact features from raw visual inputs and UAV states,which then feed into a policy network.We evaluated our approach in a simulated environment featuring a challenging circular trajectory with eight gate obstacles.The results demonstrate that the policy trained with pure behavior cloning consistently failed.In stark contrast,our DAgger-augmented behavior cloning method successfully traversed all gates without collision.Our findings confirm that DAgger effectively mitigates the shortcomings of behavior cloning,enabling the creation of reliable and sample-efficient navigation policies for UAVs.展开更多
Vehicle re-identification(ReID)is a challenging task in intelligent transportation,and urban surveillance systems due to its complications in camera viewpoints,vehicle scales,and environmental conditions.Recent transf...Vehicle re-identification(ReID)is a challenging task in intelligent transportation,and urban surveillance systems due to its complications in camera viewpoints,vehicle scales,and environmental conditions.Recent transformer-based approaches have shown impressive performance by utilizing global dependencies,these models struggle with aspect ratio distortions and may overlook fine-grained local attributes crucial for distinguishing visually similar vehicles.We introduce a framework based on Swin Transformers that addresses these challenges by implementing three components.First,to improve feature robustness and maintain vehicle proportions,our Aspect Ratio-Aware Swin Transformer(AR-Swin)preserve the native ratio via letterbox,uses a non-square(16×8)patch-embedding stem,and keeps fixed 7×7 token windows.Second,we introduce a Dynamic Feature Fusion Network(DFFNet)that adaptively integrates global Swin features with local attribute embeddings;such as color and vehicle type enablingmore discriminative representations.Third,our Regional Attention Blocks incorporate regionalmasks into the transformer’s windowed attentionmechanism,effectively highlighting critical details like manufacturer logos or lights.On VeRi-776,we obtain 82.55 mAP,97.26 Rank-1 and 99.23 Rank-5,and on VehicleID we obtain 91.8 Rank-1 and 97.75 Rank-5.The design is drop-in for Swin backbones and emphasizes robustness without increasing architectural complexity.Code:https://github.com/sft110/Swinvreid.展开更多
Hypersonic morphing vehicle(HMV)can reconfigure aerodynamic geometries in real time,adapting to diverse needs like multi-mission profiles and wide-speed-range flight,spanwise morphing and sweep angle variation are rep...Hypersonic morphing vehicle(HMV)can reconfigure aerodynamic geometries in real time,adapting to diverse needs like multi-mission profiles and wide-speed-range flight,spanwise morphing and sweep angle variation are representative large-scale wing reconfiguration modes.To meet the HMV's need for an increased lift and a lift to drag ratio during hypersonic maneuverability and cruise or reentry equilibrium glide,this paper proposes an innovative single-DOF coupled morphing-wing system.We then systematically analyze its open-loop kinematics and closed-loop connectivity constraints,and the proposed system integrates three functional modules:the preset locking/release mechanism,the coupled morphing-wing mechanism,and the integrated wing locking with active stiffness control mechanism.Experimental validation confirms stable,continuous morphing under simulated aerodynamic loads.The experimental results indicate:(i)SMA actuators exhibit response times ranging from 18 s to 160 s,providing sufficient force output for wing unlocking;(ii)The integrated wing locking with active stiffness control mechanism effectively secures wing positions while eliminating airframe clearance via SMA actuation,improving the first-order natural frequency by more than 17%;(iii)The distributed aerodynamic loading system enables precise multi-stage follow-up loading during morphing,with the coupled morphing wing maintaining stable,continuous operation under 0-3500 N normal loads and 110-140 N axial force.The proposed single-DOF coupled morphing mechanism not only simplifies and improves structural efficiency but also demonstrates superior performance in locking control,stiffness enhancement,and aerodynamic responsiveness.This establishes a foundational framework for the design of future intelligent morphing configurations and the implementation of flight control systems.展开更多
Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instabili...Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.展开更多
Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adap...Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adaptability to maneuver mode mutations,a trajectory prediction method is proposed that integrates online maneuver mode identification with dynamic modeling.Characteristic parameters are extracted from tracking data for parameterized modeling,enabling real-time identification of maneuver modes.In addition,a maneuver detection mechanism based on higher-order cumulants is introduced to detect lateral maneuver mutations and optimize the use of historical data.Simulation results show that the proposed method achieves accurate trajectory prediction during the glide phase and maintains high accuracy under maneuver mutations,significantly enhancing the prediction performance of both three-dimensional trajectories and ground tracks.展开更多
This study aims to establish an integrated sensitivity analysis framework for optimization and design of the dynamic performance of mechanical systems such as tracked vehicles,by combining the direct differentiation m...This study aims to establish an integrated sensitivity analysis framework for optimization and design of the dynamic performance of mechanical systems such as tracked vehicles,by combining the direct differentiation method(DDM)with the linear multibody system transfer matrix method(linear MSTMM).The rigid-flexible coupled multibody system dynamics model of a tracked vehicle is established using the linear MSTMM and validated through the modal test.Building upon the existing DDM-based eigenvalue sensitivity analysis method within the linear MSTMM,the DDM is embedded into it to enable programmable and efficient computation of dynamic response sensitivities for mechanical systems.The proposed approach is used to quantitatively evaluate the sensitivities of both natural vibration characteristics(e.g.,natural frequencies and mode shapes)and transient dynamic responses of the tracked vehicle with respect to system parameters,successfully identifying critical structural parameters.Compared to conventional finite difference methods,the developed methodology eliminates sensitivity to perturbation step sizes.The contributions of this work lie in establishing a unified theoretical foundation and analysis framework for guiding dynamics optimization and design of mechanical systems,and extending the applicability of the linear MSTMM to sensitivity analysis of transient dynamic responses.展开更多
基金supported in part by the National Natural Science Foundation of China(U25A20473,62222314)the YanZhao Young Scientist Project of Hebei Province(F2024203047)+2 种基金the Natural Science Foundation of Hebei Province(F2022203001,F2024203072)the State Key Laboratory of Submarine Geoscience(sglkt2025-7)the Education Department Foundation of Hebei Province(JCZX2025027)。
文摘Dear Editor,This letter studies the motion planning issue for an autonomous underwater vehicle(AUV)in obstacle environment.We propose a novel integrated detection-communication waveform that enables simultaneous obstacle detection and self-localization.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)-Information Technology Research Center(ITRC)under Grant No.IITP-2025-RS-2023-00259004the Basic Science Research Program through the National Research Foundation of Korea(NRF)under Grant No.RS-2025-25434261.
文摘The intelligent transportation systems require secure,low-latency,and reliable communication architectures to enable the real-time vehicular application.This paper proposes an edge-intelligent semantic aggregation(EISA)framework for 6G unmanned aerial vehicle(UAV)-assisted Internet of vehicles(IoV)networks that integrates task-driven semantic communication,deep reinforcement learning(DRL)-based edge intelligence,and blockchain-based semantic validation across 6G terahertz(THz)links.UAVs in the proposed architecture serve as adaptive edge nodes that receive semantically vital information about the vehicle at any given stage,optimize aggregation and transmission parameters dynamically,and guarantee data integrity through a structured,lightweight consortium blockchain that signs semantically detailed representations rather than raw packets.Simulation results from a hybrid NS-3,MATLAB,and Python environment indicate that the proposed framework can achieve up to 45%reduction in end-to-end latency,an approximately 70%increase in throughput,and semantic efficiency with blockchain verification delays of less than 20 ms(more than 98%).These findings support the effectiveness of the proposed co-design for achieving context-aware,energy-efficient,and reliable communication under heavy-traffic conditions.The proposed framework provides a flexible and scalable foundation for next-generation 6G-enabled automotive networks,with subsequent growth toward federated learning-based collaborative intelligence,digital-twinassisted traffic modeling,and quantum-safe blockchain mechanisms to enhance scalability,intelligence,and long-term security.
基金supported by the National key research and development program of China(No.2022YFE0135000)the National Natural Science Foundation of China(No.42175123)the Natural Science Foundation of Tianjin(No.23JCJQJC00170).
文摘Ammonia(NH3)has been widely recognized as a key precursor of atmospheric secondary aerosol formation.Vehicle emission is a major source of urban atmospheric NH3.With the tightening of emission standards and the growing trend of vehicle fleet electrification,it is imperative to update the emission factors for NH3 from real-world on-road fleets.In this study,a tunnel measurement was conducted in the urban area of Tianjin,China.The fleet-average NH3 emission factor(EF)was 11.2 mg/(km·veh),significantly lower than those in previous studies,showing the benefit of emission standard updating.Through a multiple linear regression analysis,the EFs of light-duty gasoline vehicles,light-duty diesel vehicles,and heavy-duty diesel vehicles(HDDVs)were estimated to be 5.7±0.6 mg/(km·veh),40.8±5.1 mg/(km·veh),and 160.2±16.6 mg/(km·veh),respectively.Based on the results from this study,we found that HDDVs,which comprise<3%of the total vehicles may contribute approximately 22%of total NH3 emissions in Tianjin.Our results highlight NH3 emissions from HDDVs,a previously potentially overlooked source of NH3 emissions in urban areas.The actual on-road NH3 emissions from HDDVs may exceed current expectations,posing a growing concern for the future.
文摘The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.
基金supported by the National Natural Science Foundation of China(62421004,U24A20279,62473243,62533004)。
文摘Dear Editor,This letter addresses the formation control problem for unmanned surface vehicles(USVs)under GPS-denied environments.A novel visual servo formation control scheme,utilizing a monocular camera on the follower to obtain the leader’s global position,is developed,which is also capable of guaranteeing collision avoidance and visibility maintenance(CA&VM)raised by the requirement of actual formation navigation.
文摘Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.
文摘To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.
文摘According to the China Association of Automobile Manufacturers (CAAM),China's auto industry reached record highs in 2025,with production and sales at 34.53 million and 34.4 million vehicles,respectively.This secured China's position as the world's largest auto market for the 17th year in a row.
基金supported by the CRRC Original Technology TenYear Cultivation Program(Grant No.2022CYY007)。
文摘Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering.This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies,aiming to develop a fatigue life prediction method for critical bogie components using deep learning models and measured track load spectra.Extensive field tests on Chengdu Metro Line 1 were conducted to acquire acceleration and stress response data of the wheelset lifting lug,generating training samples for the neural network system.Component stress responses were calculated via time-domain track acceleration and validated against in-situ stress measurements.Results show that neural network-fitted dynamic stress values exhibit excellent consistency with measured data,with errors constrained within 5%.This study validates the proposed small-sample deep learning approach as an effective and accurate solution for fatigue life prediction of critical bogie components under operational load conditions.
基金supported in part by 14th Five Year National Key R&D Program Project(Project Number:2023YFB3211001)the National Natural Science Foundation of China(62273339,U24A201397).
文摘Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.
文摘The radiative heat flux of the plume from reusable rockets is a critical parameter during the launch and return processes.This paper proposes a method for calculating radiative heat flux with higher accuracy than previously reported for a recoverable nine-engine liquid-propellant rocket.Based on the Radiative Transfer Equation(RTE),this study employs the discrete transfer method to solve the transient RTE problem using physical properties to describe the problem while avoiding the need to directly solve mathematical equations.The proposed method can effectively determine the radiative heat flux of the flow field and is applicable to problems involving various geometries.Calculations reveal that during the ascent phase of the rocket,the radiative heat flux at the base of the vehicle reaches its maximum in the initial stages of the lift-off,reaching a maximum of~50 kW/m^(2),which is 2.24 times the maximum value during the return phase.During the deceleration stage of re-entry into the atmosphere,the maximum radiative heat flux recorded on the sidewall of the rocket is 29.1 kW/m^(2);the maximum heat flux on the bottom surface is approximately 22.3 kW/m^(2),accounting for 76.6%of that on the rocket's sidewall.This provides a basis for the thermal protection design of the rocket's bottom and walls as well as for the thermal management of cryogenic propellant tanks.Future research will involve ground engine testing and flight experiments to further validate the proposed model.
基金supported in part by the Research on Key Technologies for the Development of an Active Balancing Cooperative Control Systemfor Distribution Networks and the National Natural Science Foundation of China under Grant 521532240029,Grant 62303006.
文摘To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates.
基金supported by the Korea Institute of Police Technology(No.:RS-2024-00405603).
文摘Autonomous vehicles operate without direct human intervention,which introduces safety risks that differ from those of conventional vehicles.Although many studies have examined safety issues related to autonomous driving,high-risk situations have often been defined using single indicators,making it difficult to capture the complex and evolving nature of accident risk.To address this limitation,this study proposes a structured framework for defining and analyzing high-risk situations throughout the traffic accident process.High-risk situations are described using three complementary indicators:accident likelihood,accident severity,and accident duration.These indicators explain how risk emerges,increases,and persists over time.Based on this concept,a framework for traffic accident visualization analysis is developed to support phase-specific risk assessment and visualization.The framework combines accident-phase information with factor-level risk contributions,allowing systematic identification of key factors and their interactions across different accident stages.Using combinations of the three indicators,high-risk situations are classified into twenty-seven distinct types,providing a clear typology for complex accident scenarios involving autonomous vehicles.The applicability of the proposed framework is demonstrated through two representative accident scenarioswith different risk characteristics.The results showthat the framework effectively captures interactions among multiple risk factors,explains how risk levels change from pre-crash to post-crash phases,and identifies contributing factors that are difficult to detect using conventional traffic accident investigation methods.Overall,the proposed framework offers a practical basis for autonomous vehicle accident analysis,safety evaluation,and policy-related decision-making.
基金supported by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology.
文摘With the increasing complexity of logistics operations,traditional static vehicle routing models are no longer sufficient.In practice,customer demands often arise dynamically,and multi-depot systems are commonly used to improve efficiency.This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand.New customers appear in the delivery process at any time and are periodically optimized according to time slices.Then,we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem.The classical ant colony algorithm uses spatial distance to select nodes,while TS-ACO considers the impact of both temporal and spatial distance on node selection.Meanwhile,we adopt Cordeau’s Multi-Depot Vehicle Routing Problem with Time Windows(MDVRPTW)dataset to evaluate the performance of our system.According to the experimental results,TS-ACO,which considers spatial and temporal distance,is more effective than the classical ACO,which only considers spatial distance.
基金supported by the National Natural Science Foundation of China(No.62576349)。
文摘Unmanned aerial vehicles(UAVs)face the challenge of autonomous obstacle avoidance in complex,multi-obstacle environments.Behavior cloning offers a promising approach to rapidly acquire a learning policy from limited expert demonstrations.However,pure imitation learning inherently suffers from poor exploration and limited generalization,typically necessitating extensive datasets to train competent student policies.We utilize a cross-modal variational autoencoder(CM-VAE)to extract compact features from raw visual inputs and UAV states,which then feed into a policy network.We evaluated our approach in a simulated environment featuring a challenging circular trajectory with eight gate obstacles.The results demonstrate that the policy trained with pure behavior cloning consistently failed.In stark contrast,our DAgger-augmented behavior cloning method successfully traversed all gates without collision.Our findings confirm that DAgger effectively mitigates the shortcomings of behavior cloning,enabling the creation of reliable and sample-efficient navigation policies for UAVs.
基金supported by SDAIA-KFUPM Joint Research Center of Artificial Intelligence,Deanship of Research,King Fahd University of Petroleum and Minerals,under Grant#CAI02562(JRC-AI-RFP-17).
文摘Vehicle re-identification(ReID)is a challenging task in intelligent transportation,and urban surveillance systems due to its complications in camera viewpoints,vehicle scales,and environmental conditions.Recent transformer-based approaches have shown impressive performance by utilizing global dependencies,these models struggle with aspect ratio distortions and may overlook fine-grained local attributes crucial for distinguishing visually similar vehicles.We introduce a framework based on Swin Transformers that addresses these challenges by implementing three components.First,to improve feature robustness and maintain vehicle proportions,our Aspect Ratio-Aware Swin Transformer(AR-Swin)preserve the native ratio via letterbox,uses a non-square(16×8)patch-embedding stem,and keeps fixed 7×7 token windows.Second,we introduce a Dynamic Feature Fusion Network(DFFNet)that adaptively integrates global Swin features with local attribute embeddings;such as color and vehicle type enablingmore discriminative representations.Third,our Regional Attention Blocks incorporate regionalmasks into the transformer’s windowed attentionmechanism,effectively highlighting critical details like manufacturer logos or lights.On VeRi-776,we obtain 82.55 mAP,97.26 Rank-1 and 99.23 Rank-5,and on VehicleID we obtain 91.8 Rank-1 and 97.75 Rank-5.The design is drop-in for Swin backbones and emphasizes robustness without increasing architectural complexity.Code:https://github.com/sft110/Swinvreid.
基金supported by the National Natural Science Foundation of China(Grant No.52405257)the China Postdoctoral Science Foundation(Grant No.2024M764201).
文摘Hypersonic morphing vehicle(HMV)can reconfigure aerodynamic geometries in real time,adapting to diverse needs like multi-mission profiles and wide-speed-range flight,spanwise morphing and sweep angle variation are representative large-scale wing reconfiguration modes.To meet the HMV's need for an increased lift and a lift to drag ratio during hypersonic maneuverability and cruise or reentry equilibrium glide,this paper proposes an innovative single-DOF coupled morphing-wing system.We then systematically analyze its open-loop kinematics and closed-loop connectivity constraints,and the proposed system integrates three functional modules:the preset locking/release mechanism,the coupled morphing-wing mechanism,and the integrated wing locking with active stiffness control mechanism.Experimental validation confirms stable,continuous morphing under simulated aerodynamic loads.The experimental results indicate:(i)SMA actuators exhibit response times ranging from 18 s to 160 s,providing sufficient force output for wing unlocking;(ii)The integrated wing locking with active stiffness control mechanism effectively secures wing positions while eliminating airframe clearance via SMA actuation,improving the first-order natural frequency by more than 17%;(iii)The distributed aerodynamic loading system enables precise multi-stage follow-up loading during morphing,with the coupled morphing wing maintaining stable,continuous operation under 0-3500 N normal loads and 110-140 N axial force.The proposed single-DOF coupled morphing mechanism not only simplifies and improves structural efficiency but also demonstrates superior performance in locking control,stiffness enhancement,and aerodynamic responsiveness.This establishes a foundational framework for the design of future intelligent morphing configurations and the implementation of flight control systems.
基金Supported by the National Defense Basic Scientific Research Program of China.
文摘Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.
基金supported by the National Natural Science Foundation of China(12302056)the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZC20233445)。
文摘Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adaptability to maneuver mode mutations,a trajectory prediction method is proposed that integrates online maneuver mode identification with dynamic modeling.Characteristic parameters are extracted from tracking data for parameterized modeling,enabling real-time identification of maneuver modes.In addition,a maneuver detection mechanism based on higher-order cumulants is introduced to detect lateral maneuver mutations and optimize the use of historical data.Simulation results show that the proposed method achieves accurate trajectory prediction during the glide phase and maintains high accuracy under maneuver mutations,significantly enhancing the prediction performance of both three-dimensional trajectories and ground tracks.
基金supported by the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20241443)the Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2024ZB072)the National Natural Science Foundation of China(Grant No.92266201).
文摘This study aims to establish an integrated sensitivity analysis framework for optimization and design of the dynamic performance of mechanical systems such as tracked vehicles,by combining the direct differentiation method(DDM)with the linear multibody system transfer matrix method(linear MSTMM).The rigid-flexible coupled multibody system dynamics model of a tracked vehicle is established using the linear MSTMM and validated through the modal test.Building upon the existing DDM-based eigenvalue sensitivity analysis method within the linear MSTMM,the DDM is embedded into it to enable programmable and efficient computation of dynamic response sensitivities for mechanical systems.The proposed approach is used to quantitatively evaluate the sensitivities of both natural vibration characteristics(e.g.,natural frequencies and mode shapes)and transient dynamic responses of the tracked vehicle with respect to system parameters,successfully identifying critical structural parameters.Compared to conventional finite difference methods,the developed methodology eliminates sensitivity to perturbation step sizes.The contributions of this work lie in establishing a unified theoretical foundation and analysis framework for guiding dynamics optimization and design of mechanical systems,and extending the applicability of the linear MSTMM to sensitivity analysis of transient dynamic responses.
