This study focuses on developing a deep learning model capable of recognizing vehicle brands and models,integrated with a law enforcement intelligence platform to overcome the limitations of existing license plate rec...This study focuses on developing a deep learning model capable of recognizing vehicle brands and models,integrated with a law enforcement intelligence platform to overcome the limitations of existing license plate recognition techniques—particularly in handling counterfeit,obscured,or absent plates.The research first entailed collecting,annotating,and classifying images of various vehiclemodels,leveraging image processing and feature extraction methodologies to train themodel on Microsoft Custom Vision.Experimental results indicate that,formost brands and models,the system achieves stable and relatively high performance in Precision,Recall,and Average Precision(AP).Furthermore,simulated tests involving illicit vehicles reveal that,even in cases of reassigned,concealed,or missing license plates,the model can rely on exterior body features to effectively identify vehicles,reducing dependence on plate-specific data.In practical law enforcement scenarios,these findings can accelerate investigations of stolen or forged plates and enhance overall accuracy.In conclusion,continued collection of vehicle images across broadermodel types,production years,and modification levels—along with refined annotation processes and parameter adjustment strategies—will further strengthen themethod’s applicability within law enforcement intelligence platforms,facilitating more precise and comprehensive vehicle recognition and control in real-world operations.展开更多
Significant diurnal temperature variations in mountainous rack railways cause stiffness mismatches between the rack structure and simply supported bridges,leading to critical failures like bolt loosening and rack frac...Significant diurnal temperature variations in mountainous rack railways cause stiffness mismatches between the rack structure and simply supported bridges,leading to critical failures like bolt loosening and rack fractures.This study develops a dynamic model of the vehicle-rack-bridge system based on train-track-bridge interaction theory,integrating gear-rack meshing and wheel-rail contact mechanisms.The model analyzes the dynamic response of bridges with varying spans under combined thermal and dynamic loading.Numerical simulations,conducted using finite element analysis,reveal peak vibration accelerations of 1.3 m/s^(2)for the rack,3.0 m/s^(2)for the rail,1.2 m/s^(2)for the sleeper,and 0.1 m/s^(2)for the bridge,with maximum stresses of 3 MPa in the rack,8 MPa in the rail,and 25 MPa in connecting bolts.The results show significant span-dependent amplification of stress and strain in the rack system under thermo-mechanical loading,exceeding material strength limits at 60-meter spans.An innovative elastic connection method is proposed to mitigate stress concentrations effectively,en-hancing system durability.This study introduces a novel approach to modeling complex thermo-mechanical interactions in rack railway systems,validated through extensive simulations,and provides a practical solution for improving structural resilience,offering theoretical guidance for optimizing rack-bridge system design to ensure operational safety in extreme environmental conditions.展开更多
Research has shown that when vehicles follow the Ackerman steering principle(ASP),the tire wear can be reduced and the path tracking performance can be improved.However,in the case of four-wheel independent steering(4...Research has shown that when vehicles follow the Ackerman steering principle(ASP),the tire wear can be reduced and the path tracking performance can be improved.However,in the case of four-wheel independent steering(4WIS)vehicles,the steering systems of the four wheels are relatively independent,and there are differences and uncertainties in individual steering dynamics,which lead to challenges for all four wheels in simultaneously satisfying the ASP and may deteriorate the vehicle path tracking performance.In response to this problem,this paper introduces a four-wheel consistent coordinated steering control for 4WIS vehicles.The algorithm innovatively reconfigures the Ackerman steering relationships as coupling constraints among the wheels,and utilizes the constraint-following method to design controller.The controller achieves uniform boundedness(UB)and uniform ultimate boundedness(UUB)of ASP constraint error.The Carsim/Simulink joint simulation results demonstrate that the algorithm guarantees the approximate satisfaction of ASP in both the transient and steady-state of the vehicle path tracking.Also,it significantly improves the path tracking performance.展开更多
Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking proce...Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking process often encounters challenges in learning efficiency and generalization.To address this issue,this paper designs a deep deterministic policy gradient(DDPG)-based reinforcement learning strategy that integrates imitation learning and feedforward exploration in the path following process.In imitation learning,the path tracking control data generated by the model predictive control(MPC)method is used to train an end-to-end steering control model of a deep neural network.Another feedforward exploration behavior is predicted by road curvature and vehicle speed,and adds it and imitation learning to the DDPG reinforcement learning to obtain decision-making experience and action prediction behavior of the path tracking process.In the reinforcement learning process,imitation learning is used to update the pre-training parameters of the actor network,and a feedforward steering technique with random noise is adopted for strategy exploration.In the reward function,a hierarchical progressive reward form and a constrained objective reward function referring to MPC are designed,and the actor-critic network architecture is determined.Finally,the path tracking performance of the designed method is verified by comparing various training results,simulations,and HIL tests.The results show that the designed method can effectively utilize pre-training and feedforward prior experience to obtain optimal path tracking performance of an autonomous vehicle,and has better generalization ability than other methods.This study provides an efficient control scheme for improving the end-to-end control performance of autonomous vehicles.展开更多
Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collect...Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operati...Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.展开更多
The synergistic effects of corrosion and impact loading on the microstructure evolution and dynamic mechanical properties of ultrahigh-strength AerMet 100 steel are investigated.Through integrated experiments and mode...The synergistic effects of corrosion and impact loading on the microstructure evolution and dynamic mechanical properties of ultrahigh-strength AerMet 100 steel are investigated.Through integrated experiments and modeling,the result reveals that the corrosion leads to grain refinement and a reduction in the proportion of low-angle grain boundaries.Notably,corrosion promotes austenite enrichment(increasing from 1.8%to 13.9%)through selective dissolution of the martensitic matrix,while repetitive impacts reverse this trend(reducing to 0.1%)through stress-induced martensitic transformation.Fracture analysis demonstrates corrosion-induced ductile-to-brittle transition,with quasi-cleavage features dominating after prolonged corrosion.A physics-based dynamic yield strength model with<3%prediction error relative to impact tests is developed.These findings establish microstructure-property relationships of AerMet 100 steel under multi-field coupling,providing critical guidance for designing corrosion-resistant ultrahigh-strength steels in marine-impact environments.展开更多
(NbZrHfTi)C high-entropy ceramics,as an emerging class of ultra-high-temperature materials,have garnered significant interest due to their unique multi-principal-element crystal structure and exceptional hightemperatu...(NbZrHfTi)C high-entropy ceramics,as an emerging class of ultra-high-temperature materials,have garnered significant interest due to their unique multi-principal-element crystal structure and exceptional hightemperature properties.This study systematically investigates the mechanical properties of(NbZrHfTi)C high-entropy ceramics by employing first-principles density functional theory,combined with the Debye-Grüneisen model,to explore the variations in their thermophysical properties with temperature(0–2000 K)and pressure(0–30 GPa).Thermodynamically,the calculated mixing enthalpy and Gibbs free energy confirm the feasibility of forming a stable single-phase solid solution in(NbZrHfTi)C.The calculated results of the elastic stiffness constant indicate that the material meets the mechanical stability criteria of the cubic crystal system,further confirming the structural stability.Through evaluation of key mechanical parameters—bulk modulus,shear modulus,Young’s modulus,and Poisson’s ratio—we provide comprehensive insight into the macro-mechanical behaviour of the material and its correlation with the underlying microstructure.Notably,compared to traditional binary carbides and their average properties,(NbZrHfTi)C exhibits higher Vickers hardness(Approximately 28.5 GPa)and fracture toughness(Approximately 3.4 MPa⋅m^(1/2)),which can be primarily attributed to the lattice distortion and solid-solution strengthening mechanism.The study also utilizes the quasi-harmonic approximation method to predict the material’s thermophysical properties,including Debye temperature(initial value around 563 K),thermal expansion coefficient(approximately 8.9×10^(−6) K−1 at 2000 K),and other key parameters such as heat capacity at constant volume.The results show that within the studied pressure and temperature ranges,(NbZrHfTi)C consistently maintains a stable phase structure and good thermomechanical properties.The thermal expansion coefficient increasing with temperature,while heat capacity approaches the Dulong-Petit limit at elevated temperatures.These findings underscore the potential of(NbZrHfTi)C applications in ultra-high temperature thermal protection systems,cutting tool coatings,and nuclear structural materials.展开更多
3D printing,as a versatile additive manufacturing technique,offers high design flexibility,rapid prototyping,minimal material waste,and the capability to fabricate complex,customized geometries.These attributes make i...3D printing,as a versatile additive manufacturing technique,offers high design flexibility,rapid prototyping,minimal material waste,and the capability to fabricate complex,customized geometries.These attributes make it particularly well-suited for low-temperature hydrogen electrochemical conversion devices—specifically,proton exchange membrane fuel cells,proton exchange membrane electrolyzer cells,anion exchange membrane electrolyzer cells,and alkaline electrolyzers—which demand finely structured components such as catalyst layers,gas diffusion layers,electrodes,porous transport layers,and bipolar plates.This review provides a focused and critical summary of the current progress in applying 3D printing technologies to these key components.It begins with a concise introduction to the principles and classifications of mainstream 3D printing methods relevant to the hydrogen energy sector and proceeds to analyze their specific applications and performance impacts across different device architectures.Finally,the review identifies existing technical challenges and outlines future research directions to accelerate the integration of 3D printing in nextgeneration low-temperature hydrogen energy systems.展开更多
In this study,by adjusting the homogenization process,numerous lamellar-shapedγ’phases are generated and uniformly distributed throughout the grain interior within as-extruded Mg-9Gd-4Y-1Zn-0.5Zr(wt.%)alloy,leading ...In this study,by adjusting the homogenization process,numerous lamellar-shapedγ’phases are generated and uniformly distributed throughout the grain interior within as-extruded Mg-9Gd-4Y-1Zn-0.5Zr(wt.%)alloy,leading to a remarkable increase enhancement in both tensile strength and fracture toughness.Notably,as compared to the alloy containing block-shaped long-period stacking-ordered(LPSO)phase,when the lamellar-shapedγ’phase is introduced within theα-Mg matrix,the fracture toughness of 29.7 MPa·m^(1/2)can be achieved with a 27%improvement.This superior fracture resistance is mainly attributed to the delamination toughening derived from the intensive micro-cracks occurring alongγ’phase interfaces oriented perpendicular to the primary fracture surface.Owing to the presence of lamellarshapedγ’phase,the fracture morphology can be significantly changed and characterized with deep dimples and pronounced deflection of main crack,which collectively contribute to the enhanced plastic energy dissipation and fracture toughness.The characteristics of deformed microstructure near the fracture surface demonstrate the activation of kinking and the inhibition of twin propagation due to the interactions with lamellarγ’phase.Such deformation behavior can effectively impede the crack propagation and contribute to the superior fracture resistance.Besides,the X-ray computed tomography analysis of the fractured alloy exhibits the distribution and size of voids,indicating that the prolate voids preferentially nucleate and propagate parallel to the lamellarγ’phase.Accordingly,the deformation mechanisms under a triaxial stress state involve the intricate interplay between lamellarγ’phase-induced delamination,crack deflection as well as void formation.Through the application of tailored pre-treatment heat treatment processes,the control of phase constituents within the microstructure can be achieved to improve the mechanical properties of Mg alloys.It is anticipated to provide a comprehensive understanding of the fracture behavior of Mg-Gd-Y-Zn-Zr,with particular emphasis on the synergistic effects of lamellarγ’phase and LPSO phase in the optimization of overall mechanical performance.展开更多
In order to study the influence of thermal deformation of long-span cable- stayed bridge (LSCSB) on the dynamic characteristics of metro vehicle on the bridge, based on the theory of vehicle-track coupled dynamic...In order to study the influence of thermal deformation of long-span cable- stayed bridge (LSCSB) on the dynamic characteristics of metro vehicle on the bridge, based on the theory of vehicle-track coupled dynamics, the rigid-flexible coupled dynamic model of metro vehicle-track-LSCSB system is established by using finite element method and multi-rigid-body dynamics. Adopting this model, the deformation of LSCSB subject to temperature is analyzed, then the comprehensive effect of track random irregularity and rail deformation caused by temperature load is considered to study the dynamic characteristics of metro vehicle running through the bridge, and finally the influences of temperature increment and running speed on concerned dynamic indices of vehicle are studied. The results show that the LSCSB deforms obviously subject to temperature load, and the overall performance is that the cooling is arched, and the heating is bent, and the shape variable changes almost linearly with the temperature load. According to the parameters studied in this paper, the rail deformation caused by temperature load increases the wheel-rail vertical force, derailment coefficient and wheel load reduction rate by 1.5%, 3.1% and 5% respectively. The vertical acceleration of the vehicle body decreases by 2.4% under the cooling condition, while increases by 3.7% under the heating condition. The dynamic response of the bridge changes under temperature load. The maximum vertical and horizontal displacement in the middle of the main beam span are 6.24 mm and 2.19 mm respectively, and the maximum vertical and horizontal acceleration are 1.29 cm/s<sup>2</sup> and 2.54cm/s<sup>2</sup> respectively. The derailment coefficient and vertical acceleration of vehicle body are more affected by temperature load, and the wheel load reduction rate and wheel-rail vertical force are more affected by speed. The conclusion of this paper provides a reference for subsequent scholars to study the influence of thermal deformation on the dynamic response of vehicles on LSCSB.展开更多
Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightw...Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightweight of two distinct material characteristics.MFHTWTs can achieve energy absorption through the coupling of material plastic deformation and fracture,demonstrating significant engineering value in passive safety.This review provides a comprehensive examination of the crashworthiness topology optimization of MFHTWTs,aiming to demonstrate that a deeply integrated approach combining topology and parameter opti-mization can realize an optimal design method for MFHTWTs,thereby maximizing the functional utilization of limited material.Firstly,the review highlights the crashworthiness topology optimization methods(CTOMs)based on thin-walled structures.With a particular focus on metal,the review discusses both the practical ap-plicability and limitations of CTOMs under crash conditions.Additionally,based on the methodology of the equivalent static load method(ESLM),the review emphasizes that topology optimization methods considering continuous fiber paths and multi-material interface connections are also applicable to the crashworthiness op-timization of MFHTWTs.Furthermore,to couple structural parameters and configuration characteristics,in-tegrated topology optimization methods,including parameter optimization,are proposed to provide a valuable reference for the global optimization of MFHTWTs.Thus,these methods can establish the mapping relationship between key parameters and the structural energy absorption capacity.展开更多
The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a clo...The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a cloud-based cyberphysical-social systems(CPSS) framework aiming at synergizing connected automated driving. This study first introduces the CPSS and ACP-based intelligent machine systems. Then the parallel driving is proposed in the cyber-physical-social space,considering interactions among vehicles, human drivers, and information. Within the framework, parallel testing, parallel learning and parallel reinforcement learning are developed and concisely reviewed. Development on intelligent horizon(iHorizon)and its applications are also presented towards parallel horizon.The proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.展开更多
The current investigations primarily focus on using advanced suspensions to overcome the tradeo design of ride comfort and handling performance for mining vehicles. It is generally realized by adjusting spring sti nes...The current investigations primarily focus on using advanced suspensions to overcome the tradeo design of ride comfort and handling performance for mining vehicles. It is generally realized by adjusting spring sti ness or damping parameters through active control methods. However, some drawbacks regarding control complexity and uncertain reliability are inevitable for these advanced suspensions. Herein, a novel passive hydraulically interconnected suspension(HIS) system is proposed to achieve an improved ride-handling compromise of mining vehicles. A lumped-mass vehicle model involved with a mechanical–hydraulic coupled system is developed by applying the free-body diagram method. The transfer matrix method is used to derive the impedance of the hydraulic system, and the impedance is integrated to form the equation of motions for a mechanical–hydraulic coupled system. The modal analysis method is employed to obtain the free vibration transmissibilities and force vibration responses under di erent road excitations. A series of frequency characteristic analyses are presented to evaluate the isolation vibration performance between the mining vehicles with the proposed HIS and the conventional suspension. The analysis results prove that the proposed HIS system can e ectively suppress the pitch motion of sprung mass to guarantee the handling performance, and favorably provide soft bounce sti ness to improve the ride comfort. The distribution of dynamic forces between the front and rear wheels is more reasonable, and the vibration decay rate of sprung mass is increased e ectively. This research proposes a new suspension design method that can achieve the enhanced cooperative control of bounce and pitch motion modes to improve the ride comfort and handling performance of mining vehicles as an e ective passive suspension system.展开更多
A comparative study of model predictive control(MPC)schemes and robust Hstate feedback control(RSC)method for trajectory tracking is proposed in this paper.The main objective of this paper is to compare MPC and RSC co...A comparative study of model predictive control(MPC)schemes and robust Hstate feedback control(RSC)method for trajectory tracking is proposed in this paper.The main objective of this paper is to compare MPC and RSC controllers’performance in tracking predefined trajectory under different scenarios.MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire,which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode.RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison.Then,three test cases are built in CarSim-Simulink joint platform.Specifically,the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions.Besides,the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability.Furthermore,an extreme curve test is built where the road adhesion changes suddenly,in order to test the performance of both controllers under extreme conditions.Finally,the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.展开更多
Because of the limitations of electric vehicle(EV)battery technology and relevant supporting facilities,there is a great risk of breakdown of EVs during driving.The resulting driver“range anxiety”greatly affects the...Because of the limitations of electric vehicle(EV)battery technology and relevant supporting facilities,there is a great risk of breakdown of EVs during driving.The resulting driver“range anxiety”greatly affects the travel quality of EVs.These limitations should be overcome to promote the use of EVs.In this study,a method for travel path planning considering EV power supply was developed.First,based on real-time road conditions,a dynamic energy model of EVs was established considering the driving energy and accessory energy.Second,a multi-objective travel path planning model of EVs was constructed considering the power supply,taking the distance,time,energy,and charging cost as the optimization objectives.Finally,taking the actual traffic network of 15 km×15 km area in a city as the research object,the model was simulated and verified in MATLAB based on Dijkstra shortest path algorithm.The simulation results show that compared with the traditional route planning method,the total distance in the proposed optimal route planning method increased by 1.18%,but the energy consumption,charging cost,and driving time decreased by 11.62%,41.26%and 11.00%,respectively,thus effectively reducing the travel cost of EVs and improving the driving quality of EVs.展开更多
To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This st...To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This study proposes a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks. Firstly, the driving decision process is divided into intention generation and feasibility evaluations, which are realized using the utility theory and risk assessment, respectively. Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index(AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the planning stage, the lateral and roll dynamics stability domains are developed as the constraints to exclude the candidate trajectories that would cause vehicle instability. Finally, the simulation results are compared between the proposed model and the artificial potential filed model in the scenarios extracted from the naturalistic driving data. It is shown that the proposed framework can provide the human-like lane-change decisions and truck-friendly trajectories, and performs well in dynamic driving environments.展开更多
Autonomous vehicles require safe motion planning in uncertain environments,which are largely caused by surrounding vehicles.In this paper,a driving environment uncertainty-aware motion planning framework is proposed t...Autonomous vehicles require safe motion planning in uncertain environments,which are largely caused by surrounding vehicles.In this paper,a driving environment uncertainty-aware motion planning framework is proposed to lower the risk of position uncertainty of surrounding vehicles with considering the risk of rollover.First,a 4-degree of freedom vehicle dynamics model,and a rollover risk index are introduced.Besides,the uncertainty of surrounding vehicles’position is processed and propagated based on the Extended Kalman Filter method.Then,the uncertainty potential field is established to handle the position uncertainty of autonomous vehicles.In addition,the model predictive controller is designed as the motion planning framework which accounts for the rollover risk,the position uncertainty of the surrounding vehicles,and vehicle dynamic constraints of autonomous vehicles.Furthermore,two edge cases,the cut-in scenario,and merging scenario are designed.Finally,the safety,effectiveness,and real-time performance of the proposed motion planning framework are demonstrated by employing a hardware-in-the-loop experiment bench.展开更多
基金the National Science and Technology Council,Taiwan,for financially supporting this research(grant No.NSTC 114-2221-E-018-003)the Ministry of Education’s Teaching Practice Research Program,Taiwan(PSK1142780).
文摘This study focuses on developing a deep learning model capable of recognizing vehicle brands and models,integrated with a law enforcement intelligence platform to overcome the limitations of existing license plate recognition techniques—particularly in handling counterfeit,obscured,or absent plates.The research first entailed collecting,annotating,and classifying images of various vehiclemodels,leveraging image processing and feature extraction methodologies to train themodel on Microsoft Custom Vision.Experimental results indicate that,formost brands and models,the system achieves stable and relatively high performance in Precision,Recall,and Average Precision(AP).Furthermore,simulated tests involving illicit vehicles reveal that,even in cases of reassigned,concealed,or missing license plates,the model can rely on exterior body features to effectively identify vehicles,reducing dependence on plate-specific data.In practical law enforcement scenarios,these findings can accelerate investigations of stolen or forged plates and enhance overall accuracy.In conclusion,continued collection of vehicle images across broadermodel types,production years,and modification levels—along with refined annotation processes and parameter adjustment strategies—will further strengthen themethod’s applicability within law enforcement intelligence platforms,facilitating more precise and comprehensive vehicle recognition and control in real-world operations.
基金Supported by the Sichuan Science and Technology Program(Grant Nos.2021YFD0211,2023ZDZX0011).
文摘Significant diurnal temperature variations in mountainous rack railways cause stiffness mismatches between the rack structure and simply supported bridges,leading to critical failures like bolt loosening and rack fractures.This study develops a dynamic model of the vehicle-rack-bridge system based on train-track-bridge interaction theory,integrating gear-rack meshing and wheel-rail contact mechanisms.The model analyzes the dynamic response of bridges with varying spans under combined thermal and dynamic loading.Numerical simulations,conducted using finite element analysis,reveal peak vibration accelerations of 1.3 m/s^(2)for the rack,3.0 m/s^(2)for the rail,1.2 m/s^(2)for the sleeper,and 0.1 m/s^(2)for the bridge,with maximum stresses of 3 MPa in the rack,8 MPa in the rail,and 25 MPa in connecting bolts.The results show significant span-dependent amplification of stress and strain in the rack system under thermo-mechanical loading,exceeding material strength limits at 60-meter spans.An innovative elastic connection method is proposed to mitigate stress concentrations effectively,en-hancing system durability.This study introduces a novel approach to modeling complex thermo-mechanical interactions in rack railway systems,validated through extensive simulations,and provides a practical solution for improving structural resilience,offering theoretical guidance for optimizing rack-bridge system design to ensure operational safety in extreme environmental conditions.
基金Supported by National Natural Science Foundation of China(Grant Nos.52222216,52202493,52202466,U24B20124)Hunan Provincial Natural Science Foundation of China(Grant No.2022JJ40059).
文摘Research has shown that when vehicles follow the Ackerman steering principle(ASP),the tire wear can be reduced and the path tracking performance can be improved.However,in the case of four-wheel independent steering(4WIS)vehicles,the steering systems of the four wheels are relatively independent,and there are differences and uncertainties in individual steering dynamics,which lead to challenges for all four wheels in simultaneously satisfying the ASP and may deteriorate the vehicle path tracking performance.In response to this problem,this paper introduces a four-wheel consistent coordinated steering control for 4WIS vehicles.The algorithm innovatively reconfigures the Ackerman steering relationships as coupling constraints among the wheels,and utilizes the constraint-following method to design controller.The controller achieves uniform boundedness(UB)and uniform ultimate boundedness(UUB)of ASP constraint error.The Carsim/Simulink joint simulation results demonstrate that the algorithm guarantees the approximate satisfaction of ASP in both the transient and steady-state of the vehicle path tracking.Also,it significantly improves the path tracking performance.
基金Supported by National Natural Science Foundation of China(Grant No.52405104)Jiangxi Provincial Natural Science Foundation(Grant Nos.20242BAB20249 and 20232BAB204041)Science and Technology Project of Department of Transportation of Jiangxi Province(Grant No.2025QN003).
文摘Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking process often encounters challenges in learning efficiency and generalization.To address this issue,this paper designs a deep deterministic policy gradient(DDPG)-based reinforcement learning strategy that integrates imitation learning and feedforward exploration in the path following process.In imitation learning,the path tracking control data generated by the model predictive control(MPC)method is used to train an end-to-end steering control model of a deep neural network.Another feedforward exploration behavior is predicted by road curvature and vehicle speed,and adds it and imitation learning to the DDPG reinforcement learning to obtain decision-making experience and action prediction behavior of the path tracking process.In the reinforcement learning process,imitation learning is used to update the pre-training parameters of the actor network,and a feedforward steering technique with random noise is adopted for strategy exploration.In the reward function,a hierarchical progressive reward form and a constrained objective reward function referring to MPC are designed,and the actor-critic network architecture is determined.Finally,the path tracking performance of the designed method is verified by comparing various training results,simulations,and HIL tests.The results show that the designed method can effectively utilize pre-training and feedforward prior experience to obtain optimal path tracking performance of an autonomous vehicle,and has better generalization ability than other methods.This study provides an efficient control scheme for improving the end-to-end control performance of autonomous vehicles.
基金funded by the Jilin Provincial Department of Science and Technology,grant number 20230101208JC.
文摘Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金supported by the China Three Gorges Corporation(No.NBZZ202300860)the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.
基金supported by the National Natural Science Foundation of China(12522203,12532003 and U2267252)National Technological Basic Research Program of China,the Development and Application Project of Ship CAE Softwarethe Science and Technology Innovation 2035 Major Project of Yongjiang under Grant(2025Z009).
文摘The synergistic effects of corrosion and impact loading on the microstructure evolution and dynamic mechanical properties of ultrahigh-strength AerMet 100 steel are investigated.Through integrated experiments and modeling,the result reveals that the corrosion leads to grain refinement and a reduction in the proportion of low-angle grain boundaries.Notably,corrosion promotes austenite enrichment(increasing from 1.8%to 13.9%)through selective dissolution of the martensitic matrix,while repetitive impacts reverse this trend(reducing to 0.1%)through stress-induced martensitic transformation.Fracture analysis demonstrates corrosion-induced ductile-to-brittle transition,with quasi-cleavage features dominating after prolonged corrosion.A physics-based dynamic yield strength model with<3%prediction error relative to impact tests is developed.These findings establish microstructure-property relationships of AerMet 100 steel under multi-field coupling,providing critical guidance for designing corrosion-resistant ultrahigh-strength steels in marine-impact environments.
基金supported by the National Natural Science Foundation of China(Nos.92166105 and 52005053)High-Tech Industry Science and Technology Innovation Leading Program of Hunan Province(No.2020GK2085)the Science and Technology Innovation Program of Hunan Province(No.2021RC3096).
文摘(NbZrHfTi)C high-entropy ceramics,as an emerging class of ultra-high-temperature materials,have garnered significant interest due to their unique multi-principal-element crystal structure and exceptional hightemperature properties.This study systematically investigates the mechanical properties of(NbZrHfTi)C high-entropy ceramics by employing first-principles density functional theory,combined with the Debye-Grüneisen model,to explore the variations in their thermophysical properties with temperature(0–2000 K)and pressure(0–30 GPa).Thermodynamically,the calculated mixing enthalpy and Gibbs free energy confirm the feasibility of forming a stable single-phase solid solution in(NbZrHfTi)C.The calculated results of the elastic stiffness constant indicate that the material meets the mechanical stability criteria of the cubic crystal system,further confirming the structural stability.Through evaluation of key mechanical parameters—bulk modulus,shear modulus,Young’s modulus,and Poisson’s ratio—we provide comprehensive insight into the macro-mechanical behaviour of the material and its correlation with the underlying microstructure.Notably,compared to traditional binary carbides and their average properties,(NbZrHfTi)C exhibits higher Vickers hardness(Approximately 28.5 GPa)and fracture toughness(Approximately 3.4 MPa⋅m^(1/2)),which can be primarily attributed to the lattice distortion and solid-solution strengthening mechanism.The study also utilizes the quasi-harmonic approximation method to predict the material’s thermophysical properties,including Debye temperature(initial value around 563 K),thermal expansion coefficient(approximately 8.9×10^(−6) K−1 at 2000 K),and other key parameters such as heat capacity at constant volume.The results show that within the studied pressure and temperature ranges,(NbZrHfTi)C consistently maintains a stable phase structure and good thermomechanical properties.The thermal expansion coefficient increasing with temperature,while heat capacity approaches the Dulong-Petit limit at elevated temperatures.These findings underscore the potential of(NbZrHfTi)C applications in ultra-high temperature thermal protection systems,cutting tool coatings,and nuclear structural materials.
基金the support from the National Natural Science Foundation of China(Nos.22208376,UA22A20429)the Qingdao New Energy Shandong Laboratory Open Project(QNESL OP 202303)+3 种基金Shandong Provincial Natural Science Foundation(Nos.ZR2024QB175,ZR2023LFG005)Fundamental Research Funds for the Central Universities(No.25CX07002A)National Natural Science Foundation of China(Z202401390008)The Hunan Provincial Natural Science Foundation(2025JJ60301)。
文摘3D printing,as a versatile additive manufacturing technique,offers high design flexibility,rapid prototyping,minimal material waste,and the capability to fabricate complex,customized geometries.These attributes make it particularly well-suited for low-temperature hydrogen electrochemical conversion devices—specifically,proton exchange membrane fuel cells,proton exchange membrane electrolyzer cells,anion exchange membrane electrolyzer cells,and alkaline electrolyzers—which demand finely structured components such as catalyst layers,gas diffusion layers,electrodes,porous transport layers,and bipolar plates.This review provides a focused and critical summary of the current progress in applying 3D printing technologies to these key components.It begins with a concise introduction to the principles and classifications of mainstream 3D printing methods relevant to the hydrogen energy sector and proceeds to analyze their specific applications and performance impacts across different device architectures.Finally,the review identifies existing technical challenges and outlines future research directions to accelerate the integration of 3D printing in nextgeneration low-temperature hydrogen energy systems.
基金supported by National Natural Science Foundation of China(No.U21A2047,No 52071115,No 52401140,No U22A20187 and No 51971076)the China Postdoctoral Science Foundation(No.2024M760878)the Science Fund of State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle(No.32465004).
文摘In this study,by adjusting the homogenization process,numerous lamellar-shapedγ’phases are generated and uniformly distributed throughout the grain interior within as-extruded Mg-9Gd-4Y-1Zn-0.5Zr(wt.%)alloy,leading to a remarkable increase enhancement in both tensile strength and fracture toughness.Notably,as compared to the alloy containing block-shaped long-period stacking-ordered(LPSO)phase,when the lamellar-shapedγ’phase is introduced within theα-Mg matrix,the fracture toughness of 29.7 MPa·m^(1/2)can be achieved with a 27%improvement.This superior fracture resistance is mainly attributed to the delamination toughening derived from the intensive micro-cracks occurring alongγ’phase interfaces oriented perpendicular to the primary fracture surface.Owing to the presence of lamellarshapedγ’phase,the fracture morphology can be significantly changed and characterized with deep dimples and pronounced deflection of main crack,which collectively contribute to the enhanced plastic energy dissipation and fracture toughness.The characteristics of deformed microstructure near the fracture surface demonstrate the activation of kinking and the inhibition of twin propagation due to the interactions with lamellarγ’phase.Such deformation behavior can effectively impede the crack propagation and contribute to the superior fracture resistance.Besides,the X-ray computed tomography analysis of the fractured alloy exhibits the distribution and size of voids,indicating that the prolate voids preferentially nucleate and propagate parallel to the lamellarγ’phase.Accordingly,the deformation mechanisms under a triaxial stress state involve the intricate interplay between lamellarγ’phase-induced delamination,crack deflection as well as void formation.Through the application of tailored pre-treatment heat treatment processes,the control of phase constituents within the microstructure can be achieved to improve the mechanical properties of Mg alloys.It is anticipated to provide a comprehensive understanding of the fracture behavior of Mg-Gd-Y-Zn-Zr,with particular emphasis on the synergistic effects of lamellarγ’phase and LPSO phase in the optimization of overall mechanical performance.
文摘In order to study the influence of thermal deformation of long-span cable- stayed bridge (LSCSB) on the dynamic characteristics of metro vehicle on the bridge, based on the theory of vehicle-track coupled dynamics, the rigid-flexible coupled dynamic model of metro vehicle-track-LSCSB system is established by using finite element method and multi-rigid-body dynamics. Adopting this model, the deformation of LSCSB subject to temperature is analyzed, then the comprehensive effect of track random irregularity and rail deformation caused by temperature load is considered to study the dynamic characteristics of metro vehicle running through the bridge, and finally the influences of temperature increment and running speed on concerned dynamic indices of vehicle are studied. The results show that the LSCSB deforms obviously subject to temperature load, and the overall performance is that the cooling is arched, and the heating is bent, and the shape variable changes almost linearly with the temperature load. According to the parameters studied in this paper, the rail deformation caused by temperature load increases the wheel-rail vertical force, derailment coefficient and wheel load reduction rate by 1.5%, 3.1% and 5% respectively. The vertical acceleration of the vehicle body decreases by 2.4% under the cooling condition, while increases by 3.7% under the heating condition. The dynamic response of the bridge changes under temperature load. The maximum vertical and horizontal displacement in the middle of the main beam span are 6.24 mm and 2.19 mm respectively, and the maximum vertical and horizontal acceleration are 1.29 cm/s<sup>2</sup> and 2.54cm/s<sup>2</sup> respectively. The derailment coefficient and vertical acceleration of vehicle body are more affected by temperature load, and the wheel load reduction rate and wheel-rail vertical force are more affected by speed. The conclusion of this paper provides a reference for subsequent scholars to study the influence of thermal deformation on the dynamic response of vehicles on LSCSB.
基金Supported by National Natural Science Foundation of China(Grant Nos.52202431,52172353).
文摘Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightweight of two distinct material characteristics.MFHTWTs can achieve energy absorption through the coupling of material plastic deformation and fracture,demonstrating significant engineering value in passive safety.This review provides a comprehensive examination of the crashworthiness topology optimization of MFHTWTs,aiming to demonstrate that a deeply integrated approach combining topology and parameter opti-mization can realize an optimal design method for MFHTWTs,thereby maximizing the functional utilization of limited material.Firstly,the review highlights the crashworthiness topology optimization methods(CTOMs)based on thin-walled structures.With a particular focus on metal,the review discusses both the practical ap-plicability and limitations of CTOMs under crash conditions.Additionally,based on the methodology of the equivalent static load method(ESLM),the review emphasizes that topology optimization methods considering continuous fiber paths and multi-material interface connections are also applicable to the crashworthiness op-timization of MFHTWTs.Furthermore,to couple structural parameters and configuration characteristics,in-tegrated topology optimization methods,including parameter optimization,are proposed to provide a valuable reference for the global optimization of MFHTWTs.Thus,these methods can establish the mapping relationship between key parameters and the structural energy absorption capacity.
文摘The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a cloud-based cyberphysical-social systems(CPSS) framework aiming at synergizing connected automated driving. This study first introduces the CPSS and ACP-based intelligent machine systems. Then the parallel driving is proposed in the cyber-physical-social space,considering interactions among vehicles, human drivers, and information. Within the framework, parallel testing, parallel learning and parallel reinforcement learning are developed and concisely reviewed. Development on intelligent horizon(iHorizon)and its applications are also presented towards parallel horizon.The proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.
基金Supported by National Natural Science Foundation of China(Grant Nos.51805155,51675152)Foundation for Innovative Research Groups of National Natural Science Foundation of China(Grant No.51621004)Open Fund in the State Key Laboratory of Advanced Design and Manufacture for Vehicle Body(Grant No.71575005)
文摘The current investigations primarily focus on using advanced suspensions to overcome the tradeo design of ride comfort and handling performance for mining vehicles. It is generally realized by adjusting spring sti ness or damping parameters through active control methods. However, some drawbacks regarding control complexity and uncertain reliability are inevitable for these advanced suspensions. Herein, a novel passive hydraulically interconnected suspension(HIS) system is proposed to achieve an improved ride-handling compromise of mining vehicles. A lumped-mass vehicle model involved with a mechanical–hydraulic coupled system is developed by applying the free-body diagram method. The transfer matrix method is used to derive the impedance of the hydraulic system, and the impedance is integrated to form the equation of motions for a mechanical–hydraulic coupled system. The modal analysis method is employed to obtain the free vibration transmissibilities and force vibration responses under di erent road excitations. A series of frequency characteristic analyses are presented to evaluate the isolation vibration performance between the mining vehicles with the proposed HIS and the conventional suspension. The analysis results prove that the proposed HIS system can e ectively suppress the pitch motion of sprung mass to guarantee the handling performance, and favorably provide soft bounce sti ness to improve the ride comfort. The distribution of dynamic forces between the front and rear wheels is more reasonable, and the vibration decay rate of sprung mass is increased e ectively. This research proposes a new suspension design method that can achieve the enhanced cooperative control of bounce and pitch motion modes to improve the ride comfort and handling performance of mining vehicles as an e ective passive suspension system.
基金Supported by Natural Science Foundation of China(Grant Nos.52072051,51705044)Chongqing Municipal Natural Science Foundation of China(Grant No.cstc2020jcyj-msxmX0956)+1 种基金State Key Laboratory of Mechanical System and Vibration(Grant No.MSV202016)State Key Laboratory of Mechanical Transmissions(Grant No.SKLMT-KFKT-201806).
文摘A comparative study of model predictive control(MPC)schemes and robust Hstate feedback control(RSC)method for trajectory tracking is proposed in this paper.The main objective of this paper is to compare MPC and RSC controllers’performance in tracking predefined trajectory under different scenarios.MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire,which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode.RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison.Then,three test cases are built in CarSim-Simulink joint platform.Specifically,the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions.Besides,the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability.Furthermore,an extreme curve test is built where the road adhesion changes suddenly,in order to test the performance of both controllers under extreme conditions.Finally,the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.
基金Projects(51908388,51508315,51905320)supported by the National Natural Science Foundation of ChinaProject(2019 JZZY 010911)supported by the Key R&D Program of Shandong Province,China+1 种基金Project supported by the Shandong University of Technology&Zibo City Integration Develo pment Project,ChinaProject(ZR 2021 MG 012)supported by Shandong Provincial Natural Science Foundation,China。
文摘Because of the limitations of electric vehicle(EV)battery technology and relevant supporting facilities,there is a great risk of breakdown of EVs during driving.The resulting driver“range anxiety”greatly affects the travel quality of EVs.These limitations should be overcome to promote the use of EVs.In this study,a method for travel path planning considering EV power supply was developed.First,based on real-time road conditions,a dynamic energy model of EVs was established considering the driving energy and accessory energy.Second,a multi-objective travel path planning model of EVs was constructed considering the power supply,taking the distance,time,energy,and charging cost as the optimization objectives.Finally,taking the actual traffic network of 15 km×15 km area in a city as the research object,the model was simulated and verified in MATLAB based on Dijkstra shortest path algorithm.The simulation results show that compared with the traditional route planning method,the total distance in the proposed optimal route planning method increased by 1.18%,but the energy consumption,charging cost,and driving time decreased by 11.62%,41.26%and 11.00%,respectively,thus effectively reducing the travel cost of EVs and improving the driving quality of EVs.
基金supported by the National Natural Science Foundation of China(5187051675)。
文摘To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This study proposes a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks. Firstly, the driving decision process is divided into intention generation and feasibility evaluations, which are realized using the utility theory and risk assessment, respectively. Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index(AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the planning stage, the lateral and roll dynamics stability domains are developed as the constraints to exclude the candidate trajectories that would cause vehicle instability. Finally, the simulation results are compared between the proposed model and the artificial potential filed model in the scenarios extracted from the naturalistic driving data. It is shown that the proposed framework can provide the human-like lane-change decisions and truck-friendly trajectories, and performs well in dynamic driving environments.
基金National Key R&D Program of China(Grant No.2020YFB1600303)National Natural Science Foundation of China(Grant Nos.U1964203,52072215)Chongqing Municipal Natural Science Foundation of China(Grant No.cstc2020jcyj-msxmX0956).
文摘Autonomous vehicles require safe motion planning in uncertain environments,which are largely caused by surrounding vehicles.In this paper,a driving environment uncertainty-aware motion planning framework is proposed to lower the risk of position uncertainty of surrounding vehicles with considering the risk of rollover.First,a 4-degree of freedom vehicle dynamics model,and a rollover risk index are introduced.Besides,the uncertainty of surrounding vehicles’position is processed and propagated based on the Extended Kalman Filter method.Then,the uncertainty potential field is established to handle the position uncertainty of autonomous vehicles.In addition,the model predictive controller is designed as the motion planning framework which accounts for the rollover risk,the position uncertainty of the surrounding vehicles,and vehicle dynamic constraints of autonomous vehicles.Furthermore,two edge cases,the cut-in scenario,and merging scenario are designed.Finally,the safety,effectiveness,and real-time performance of the proposed motion planning framework are demonstrated by employing a hardware-in-the-loop experiment bench.
