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At present, there are few studies considering both nonlinear and fractional characteristics of suspension in vehicle systems. In this paper, a fractional nonlinear model of a quarter vehicle with two-degree-of-freedom (2-DOF) is innovatively proposed to describe the suspension system containing the viscoelastic material metal rubber. Given the lack of a general calculation scheme for the multi-degree-of-freedom fractional-order incremental harmonic balance method (IHBM), a general calculation scheme for the 2-DOF incremental harmonic balance method for nonlinear systems with fractional order is derived. The nonlinear dynamical properties of the presented model are acquired using this method. The accuracy of the proposed method is verified through a comparison with the power series expansion method. Afterward, the effects of the various parameters on the dynamic performance are analyzed. The vibration peak value of the fractional-order model is significantly higher than that of the integer-order model (IOM). Therefore, the suspension parameters should be designed with a margin when using IOM. [ABSTRACT FROM AUTHOR]

Collision accidents of construction vehicles frequently occur during the construction of building projects. It is difficult to warn of collision risk efficiently by using the safety control methods of on-site inspection and viewing surveillance video. Therefore, a novel collision risk warning model for construction vehicles is proposed in this paper. The model comprises a state awareness module and a collision risk assessment module. In the state awareness module, the YOLOv7 algorithm is used to identify and locate construction vehicles. The DeepSORT algorithm is used for the real-time tracking of construction vehicles. Finally, the state awareness module obtains a wealth of sensing information and provides it to the collision risk assessment module. In the collision risk assessment module, the speed assessment method and the minimum safety distance assessment method of construction vehicles are innovatively proposed and compiled. In the speed assessment method, the speed of construction vehicles is calculated and speeding warnings are performed using the transformation matrix between latitude and longitude coordinates and image pixel coordinates. In the minimum safety distance assessment method, the original surveillance picture is converted into a bird's eye view (BEV) of the world coordinate system through inverse perspective mapping (IPM). Then the distance of all construction vehicles is traversed, and the visual warning of the minimum safe distance is provided. The effectiveness and robustness of this collision risk warning model were verified by taking three practical projects in different construction stages as examples. The research results show that the mean average precision (mAP@0.5:0.95) of the construction vehicles collision risk warning model can reach 89.10%, and it has a good tracking effect and an efficient collision risk warning strategy. [ABSTRACT FROM AUTHOR]

Direct yaw moment control (DYC) can effectively improve the yaw stability of four-wheel distributed drive electric vehicles (4W-DDEVs) under extreme conditions, which has become an indispensable part of active safety control for 4W-DDEVs. This study proposes a novel hierarchical DYC architecture for 4W-DDEVs to enhance vehicle stability during ever-changing road conditions. Firstly, a vehicle dynamics model is established, including a two-degree-of-freedom (2DOF) vehicle model for calculating the desired yaw rate and sideslip angle as the control target of the upper layer controller, a DDEV model composed of a seven-degree-of-freedom (7DOF) vehicle model, a tire model, a motor model and a driver model. Secondly, a hierarchical DYC is designed combining the upper layer yaw moment calculation and low layer torque distribution. Specifically, based on Matlab/Simulink, improved linear quadratic regulator (LQR) with weight matrix optimization based on inertia weight cosine-adjustment particle swarm optimization (IWCPSO) is employed to compute the required additional yaw moment in the upper-layer controller, while quadratic programming (QP) is used to allocate four motors' torque with the optimization objective of minimizing the tire utilization rate. Finally, a comparative test with double-lane-change and sinusoidal conditions under a low and high adhesion road surface is conducted on Carsim and Matlab/Simulink joint simulation platform. With IWCPSO-LQR under double-lane-change (DLC) condition on a low adhesion road surface, the yaw rate and sideslip angle of the DDEV exhibits improvements of 95.2%, 96.8% in the integral sum of errors, 94.9%, 95.1% in the root mean squared error, and 78.8%, 98.5% in the peak value compared to those without control. Simulation results indicate the proposed hierarchical control method has a remarkable control effect on the yaw rate and sideslip angle, which effectively strengthens the driving stability of 4W-DDEVs. [ABSTRACT FROM AUTHOR]

Trajectory tracking on a low-speed vehicle using the model predictive control (MPC) algorithm usually assumes a simple road terrain. This assumption does not correspond to the actual road situation, leading to low tracking accuracy. Therefore, a trajectory tracking method considering road curvature based on MPC is proposed in this paper. In this method, the controller can automatically switch between MPC types. Linear model predictive control (LMPC) is selected for small road curvatures, while nonlinear model predictive control (NMPC) is employed for large road curvatures. In addition, the NMPC algorithm in this work considers the effect of road curvature on tracking accuracy, making it suitable for tracking time-varying curvature roads. To verify the feasibility of the algorithm, simulation comparisons with the basic MPC model were carried out at different testing roads and vehicle longitudinal speeds. The results indicate that the method significantly improves trajectory tracking accuracy, all while ensuring real-time calculations. The intelligent switching capability of control models based on road curvature allows its application to track trajectories on arbitrarily complex roads. [ABSTRACT FROM AUTHOR]

Carrier-free ultra-wideband sensors have high penetrability anti-jamming solid ability, which is not easily affected by the external environment, such as weather. Also, it has good performance in the complex jungle environment. In this paper, we propose a jungle vehicle identification system based on a carrier-free ultra-wideband sensor. Firstly, a composite jungle environment with the target vehicle is modeled. From this model, the simulation obtains time-domain echoes under the excitation of carrier-free ultra-wideband sensor signals in different orientations. Secondly, the time-domain signals are transformed into MTF images through the Markov transfer field to show the statistical characteristics of the time-domain echoes. At the same time, we propose an improved RepVGG network. The structure of the RepVGG network contains five stages, which consist of several RepVGG Blocks. Each RepVGG Block is created by combining convolutional kernels of different sizes using a weighted sum. We add the self-attention module to the output of stage 0 to improve the ability to extract the features of the MTF map and better capture the complex relationship between characteristics during training. In addition, a self-attention module is added before the linear layer classification output in stage 4 to improve the classification accuracy of the network. Moreover, a combined cross-entropy loss and sparsity penalty loss function helps enhance the performance and accuracy of the network. The experimental results show that the system can recognize jungle vehicle targets well. [ABSTRACT FROM AUTHOR]

A split-type two-axle vehicle for bridge indirect measurement is proposed to address the deficiencies of single-axle and conventional two-axle vehicles. At first, the conceptual design of the split-type two-axle vehicle is put forth, and the analytical solution for the vehicle-bridge interaction (VBI) system response is derived. After that, the regularity of the vehicle frequency response function is studied, and the numerical analysis method is utilized to validate the analytical solution. Based on the theoretical findings, the vehicle frequency and speed are studied to investigate their impacts on extracting bridge frequencies from vehicle responses. The results indicate that a peak will form in the frequency response function (FRF) curves when the input frequency is near to the vehicle frequency. The single peak phenomenon will occur when the zero-point condition of the FRF from the contact point to the vehicle body is remarkably close to the second vibration frequency of the vehicle. It is preferable for the first frequency of the vehicle to be higher than that of the bridge since this helps to extract the bridge frequency. On the other hand, at low speeds (≤ 43.2km/h), higher vehicle speeds will result in a greater distance between the peaks of the left- and right-shifted frequencies of the bridge, wider frequency bands, and lower spectral resolution, which is not conducive to bridge frequency identification. [ABSTRACT FROM AUTHOR]

This paper proposes a learning-based control approach for autonomous vehicles. An explicit Takagi–Sugeno (TS) controller is learned using input and output data from a preexisting controller, employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. At the same time, the vehicle model is identified in the TS model form for closed-loop stability assessment using Lyapunov theory and LMIs. The proposed approach is applied to learn the control law from an MPC controller, thus avoiding the use of online optimization. This reduces the computational burden of the control loop and facilitates real-time implementation. Finally, the proposed approach is assessed through simulation using a small-scale autonomous racing car. [ABSTRACT FROM AUTHOR]

To promote the application of automated vehicles in large airports, in this study, we present an integrated optimization method for scheduling Unmanned follow-me cars. The scheduling process is divided into three phases: Dispatch, Guidance, and Recycle. For the Dispatch phase, we establish a vehicle assignment model, to allocate the vehicle resource equitably. For the Guidance phase, we offer an quantitative way, to measure the spacing between Unmanned follow-me car and aircraft. To optimize the efficiency of airport operation in the three phases and ensure safety, the collaborative planning model, and the conflict prediction model are established. An improved grey wolf optimization algorithm is adopted to enhance the convergence speed and generalization performance. A case study at Ezhou Huahu Airport in China demonstrates the effectiveness of the methods. The results show that the model of collaborative planning can make the balance of path selection, Unmanned follow-me car's working time, and departure sequence. The convergence speed of the improved algorithm has been increased by 18.75%. The inequity index of vehicle assignment is only 0.015731, and the spatiotemporal distribution of conflicts is influenced by the airport's surface layout. [ABSTRACT FROM AUTHOR]

Accurate vehicle acceleration prediction is useful for developing reliable Advanced Driving Assistance Systems (ADAS) and improving road safety. The existence of driver heterogeneity magnifies the variations in acceleration data, leading to consequential impacts on the precision of vehicle acceleration prediction. However, few studies have fully considered the driver heterogeneity when predicting vehicle acceleration. To model the characteristics of individual drivers, this study first identifies the driving behavior semantics which is defined as the underlying patterns of driving behaviors. The analysis results from the coupled hidden Markov model (CHMM) are used to evaluate the driving behavior differences between different drivers by Wasserstein distance. Then the convolutional neural network (CNN) and long short-term memory (LSTM) network are applied to predict vehicle acceleration. To validate the accuracy of the proposed prediction framework, vehicle acceleration data in car-following conditions is extracted from the safety pilot model deployment (SPMD) dataset. The segmentation results indicate that the CHMM possesses a robust capacity for modeling driving behavior. The prediction results demonstrate that the proposed framework, which incorporates driver clustering before prediction, significantly improves the accuracy of predictions. And the CNN-LSTM outperforms the LSTM in predicting vehicle acceleration during car-following scenarios. The findings from this study can enhance the development of personalized functionalities within ADAS to promote its deployment, thereby improving its acceptance and safety. [ABSTRACT FROM AUTHOR]

In response to the strength performance requirements of the new energy Off road Vehicle steering system, this article analyzes and studies the strength performance and failure modes of the new energy Off road Vehicle steering rod based on a certain new energy Off road Vehicle model through simulation, bench experiments, and vehicle strength testing teams. Based on various vehicle misuse conditions, the force law of the steering rod under different vehicle driving conditions was analyzed, and a load spectrum for the steering rod strength bench test was developed to meet the actual vehicle usage conditions. The results indicate that the steering rod strength failure is mainly manifested as bending failure of the compression rod under alternating tension and compression conditions. Finally, based on the stability principle of the compression rod, the requirements for the strength performance index of the steering rod are proposed. The research results can provide certain technical support for the strength assessment of the vehicle steering rod. [ABSTRACT FROM AUTHOR]

This study introduces a deep learning surrogate model designed to predict the evolution of the mean pressure coefficient on the back face of a Windsor body across a range of yaw angles from 2. 5 ∘ to 10 ∘ . Utilizing a variational autoencoder (VAE), the model effectively compresses snapshots of back pressure taken at yaw angles of 2. 5 ∘ , 5 ∘ , and 10 ∘ into two latent vectors. These snapshots are derived from wall-modeled large eddy simulations (WMLESs) conducted at a Reynolds number of R e L = 2.9 × 10 6 . The frequencies that dominate the latent vectors correspond closely with those observed in both the drag's temporal evolution and the dynamic mode decomposition. The projection of the mean pressure coefficient to the latent space yields an increasing linear evolution of the two latent variables with the yaw angle. The mean pressure coefficient distribution at a yaw angle of 7. 5 ∘ is predicted with a mean error of e ¯ = 3.13 % when compared to the WMLESs results after obtaining the values of the latent space with linear interpolation. [ABSTRACT FROM AUTHOR]

With the implementation of strict emission regulations and the use of cleaner fuels, there has been a considerable reduction in exhaust emissions. However, the relative contribution of tire wear particles (TWPs) to particulate matters is expected to gradually increase. This study conducted laboratory wear experiments on tires equipped on domestically popular vehicle models, testing the factors and particle size distribution of TWPs. The results showed that the content of tire wear particle emission was mainly ultrafine particles, accounting for 94.80% of particles ranging from 6 nm to 10 μm. There were at least two concentration peaks for each test condition and sample, at 10~13 nm and 23~41 nm, respectively. The mass of TWP emission was mainly composed of fine particles and coarse particles, with concentration peaks at 0.5 μm and 1.3–2.5 μm, respectively. Both the number and mass of TWPs exhibited a bimodal distribution, with significant differences in emission intensity among different tire samples. However, there was a good exponential relationship between PM10 mass emissions from tire wear and tire camber angle. The orthogonal experimental results showed that the slip angle showed the greatest impact on TWP emission, followed by speed and load, with the smallest impact from inclination angle. [ABSTRACT FROM AUTHOR]

Several technological challenges delay the adoption of electrified powertrains in the heavy-duty transport sector. For fuel-cell hybrid electric trucks, key issues include slow cold start, reduced cooling power during high ambient temperatures, and uncertainties regarding durability. In addition, the engineers must handle the complexity of the system. In this article, a Matlab/Simulink library is introduced, which has been developed to aid engineers in the design and optimization of energy management systems and strategies of this complex system that consider mechanical, electrochemical, and thermal energy flows. The library is introduced through five example vehicle models, and through case studies that highlight the various kinds of analysis that can be performed using the provided models. All library code is open source, open for commercial use, and runs in Matlab/Simulink without any need for external libraries. [ABSTRACT FROM AUTHOR]

Aiming at the demand for subjective evaluation of driveability for fuel cell vehicles, the modeling and evaluation method of driveability for fuel cell vehicles were studied in this paper. Firstly, a real-time model of the fuel cell vehicle powertrain system was established, which included the fuel cell model, power battery model, DC/DC converter model and drive motor model. Secondly, it was integrated with the vehicle dynamics model to form a virtual prototype of a fuel cell vehicle. And a virtual subjective evaluation platform for fuel cell vehicles was built by combining the virtual prototype and high-fidelity driving simulator. Thirdly, a subjective evaluation method of driveability for fuel cell vehicles was proposed, which included the starting performance, acceleration performance, uniform speed performance and tip-in/tip-out performance. Finally, the paper used the platform and method mentioned above to conduct subjective evaluations of the fuel cell vehicles. [ABSTRACT FROM AUTHOR]

• A comprehensive vertical dynamics model of the electric vehicle considering the integrated powertrain system is proposed. • The limitations of traditional vertical dynamics model of electric vehicle in the high frequency range are revealed. • The effects of mounting stiffness and damping on the vibration of chassis and integrated powertrain system are explored. • A multi-objective optimization model is designed to minimize the vibration of chassis and integrated powertrain system. Integrated powertrain system, mainly composed of driving motor and reducer, is one of the excitation sources to electric vehicle vibration but is limited in coupling with the vertical vehicle dynamic in the existing research, which restricts the reliability of dynamics investigation of vehicle and integrated powertrain system. This work proposes a novel vertical dynamic model of the electric vehicle considering the integrated powertrain system, enabling the way to access the coupled vibration among the chassis, unsprung mass, and integrated powertrain system. The road irregularity, unbalanced magnetic pull, and gear mesh excitation are developed and are further applied to the novel vehicle dynamic model with ten degrees of freedom (DOFs). Then, the dynamic response between the novel model and traditional one is compared. Further, the effects of the stiffness and damping of suspension and mounting on the vibration of chassis and integrated powertrain system are explored by numerical calculation. Finally, a multi-objective optimization model is proposed and is driven by the dragonfly algorithm to minimize the vibration of chassis and integrated powertrain system. The research results shown that there will be present the new complex dynamic characteristics with considering the excitation from the integrated powertrain system. The suspension and mounting parameters have the nonlinear effects on the vehicle vibration. And the system vibration can be globally optimized with the optimization algorithm. [ABSTRACT FROM AUTHOR]

Research on evaluating highway bridge performance through vehicle-bridge interaction (VBI) analysis has made significant advancements. However, when assessing driving comfort, using vehicle acceleration instead of human acceleration poses challenges in accurately representing comfort. First, the paper proposes a finite element analysis method for human-vehicle-bridge spatial interactions (HVBSIs). Then, the importance of wheel path roughness difference is explored when assessing driving comfort. Furthermore, a new method for evaluating driving comfort that includes human and vehicle vibration responses has been proposed, and a simulation example of the steel-concrete composite beam bridge (SCCBB) is used to verify the effectiveness of the proposed method. The results demonstrate that the HVBSI analysis method effectively simulates the interconnected vibrations of the human body, the spatial vehicle model, and the three-dimensional (3D) bridge model. Differences in wheel path roughness significantly impact the roll vehicle vibration responses, which are crucial in driving comfort analysis. The driver's body vibration response is essential for evaluating driving comfort, and its inclusion leads to increased comfort indices values. In comparison to traditional methods, the overall vibration total value (OVTV) increases by a maximum of 109.04%, and the level of weighted vibration ( L e q ) increases by a maximum of 6.74%. This leads to an upgrade from grade IV to grade V in terms of comfort level, indicating a reduced comfort. [ABSTRACT FROM AUTHOR]

Vehicle antilock systems play a very important role in the stability and reliability during vehicle braking. Due to the complexity of the braking process, antilock braking system (ABS) usually face the problems such as nonlinearity, time-varying, and uncertain parameter modeling. Thus, aiming at the parameter model uncertainty problem of ABS, an adaptive neural network sliding mode controller (ADRBF-SMC) is designed in this paper. On this basis, establishing the quarter-vehicle model and the seven-degree-of-freedom vehicle model, and treating the difference between the two models as a kind of disturbance, carrying out vehicle braking performance simulation experiments to analyze the variation of braking performance parameters such as vehicle and wheel speeds, slip ratio, braking distance, braking torque, under the three cases of adaptive neural network sliding mode controller, traditional sliding mode controller, and no control. Simulation results show that the adaptive neural network sliding mode controller (ADRBF-SMC) proposed in this paper can play an effective control role in both vehicle dynamics models. In addition, the control method proposed in this paper has stronger anti-interference capability and higher robustness compared with the sliding mode controller (SMC). [ABSTRACT FROM AUTHOR]

This paper considers the scheduling of electric vehicles in a public transit system. Our main innovation is that we take into account that charging stations have limited capacity, while also considering partial charging. To solve the problem, we expand a connection-based network in order to track the state of charge of vehicles and model recharging actions. We then formulate the electric vehicle scheduling problem as a path-based binary program, whose linear relaxation we solve using column generation. We find integer feasible solutions using two heuristics: price-and-branch and a diving heuristic, including acceleration strategies. We test the approach using data from the concession Gooi en Vechtstreek in the Netherlands, containing up to 816 trips. The diving heuristic outperforms the other heuristic and solves the entire concession within seven hours of computation time with an optimality gap of less than 3%. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0253. [ABSTRACT FROM AUTHOR]

This paper presents the process of creating a model for electric vehicle (EV) energy consumption, enabling the rapid generation of results and the creation of energy maps. The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. These models hold particular significance for microscale road analyses. The resultant model, for test data in summer conditions, demonstrates validation indicators of an R2 of 86% and an MSE of 1.4, while, for winter conditions, its values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision makers in the management of city roads and infrastructure planners, aiding both cognitive understanding and the better planning of charging infrastructure networks. [ABSTRACT FROM AUTHOR]

Lighter-than-air (LTA) aerial vehicles such as airships and aerostats can be found in various strategic and commercial applications, primarily due to their capability to hover and stealth. The mathematical model of these vehicles helps in understanding their complex dynamics and designing and developing proper stabilisation systems for them. Stability derivatives have been used for developing mathematical models for heavier-than-air aerial vehicles since their introduction. This paper presents a methodology to develop a mathematical model of an aerostat based on stability derivatives. One of the major contributions of this study is the estimation of aerostat's added mass terms expressed as longitudinal stability derivatives due to acceleration of the longitudinal motion variables. A longitudinally decoupled linear mathematical model of a single-tethered aerostat using stability derivatives is investigated in this study. A computational fluid dynamics (CFD)-based analysis of the 3D model of the vehicle is used to obtain the stability derivatives. The methodology presented considers the aerostat and tether models separately before coupling them to create the full model. The stability derivative analysis is carried out using ANSYS Fluent, and the coupled tethered aerostat model is investigated using MATLAB 2020. The negative pitch angle of the aerostat is caused by the selection of the pitching centre as the aerostat centre of volume instead of the tether confluence point. The tension force on the tether, which is proportional to the wind velocity, and aerostat velocity components are found to be stabilised within 200–400 s. [ABSTRACT FROM AUTHOR]

This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated. [ABSTRACT FROM AUTHOR]

In the vehicle scanning method, using the amplifier as the tuned mass damper in the vehicle–bridge system has been shown to be able to scan the higher bridge frequencies clearly in the amplifier spectrum recently. To design a test vehicle enhanced by one or two amplifiers in practice, the analytical approach undoubtedly serves as the guideline. For this purpose, this study is aimed at deriving the analytical formulation for one amplifier-enhanced vehicle–bridge system and the semi-analytical formulation for two amplifier-enhanced vehicle–bridge system, while a miniature three-mass vehicle model is adopted. The correctness of the analytical formulations is verified by the finite element formulations through the dynamic responses of the amplifier, vehicle body, and bridge; good agreement is observed for all responses. The amplifier spectra are further provided to show the distinctly scanned bridge frequencies. [ABSTRACT FROM AUTHOR]

To describe the microtrajectory change behavior of two-wheeled vehicles when encountering nonrigid obstacles, this paper proposes an obstacle avoidance model of two-wheeled vehicles for nonrigid obstacles based on the improved social force method. In the model, the obstacle avoidance characteristics of two-wheeled vehicles for nonrigid obstacles and the safety operation requirements of two-wheeled vehicles are considered. The calculation methods of the minimum boundary distance of avoidance and the boundary avoidance force range edge function are proposed based on the ultimate turning angle and boundary avoidance characteristics, respectively. The speed control and direction control behavior of the rider to the vehicle is abstracted as the form of social force, and the two-wheeled vehicle/rider individual is subjected to the avoidance force and correction force perpendicular to the direction of the longitudinal axis of the vehicle body. The centrifugal trajectory obstacle avoidance motion obeying the Newtonian mechanics is studied based on the characteristics of the vehicle body turning angle change. Different traffic environments are constructed by MATLAB software and simulated according to the model running logic, and sensitivity analysis is performed based on the actual data collected in the survey. The simulation results show that, under the same conditions, compared with μ i = 50N, when μ i = 200N, the X-axis displacement of the simulated vehicle in the avoidance process is shortened by 26.31%, and the body deflection angle increases by 50.75% at the end moment of the avoidance process. Compared with electric motorcycles, the displacement of the X-axis in the avoidance process of the electric bicycle is shortened by 16.92%, and the body deflection angle at the end moment of the avoidance process is increased by 13.68%. The simulation results show that the model can well describe the trajectory change behavior characteristics of two-wheeled vehicles/riders encountering nonrigid obstacles in real situations, and the obstacle avoidance model of two-wheeled vehicles proposed in this paper is considered to be traffic justified. [ABSTRACT FROM AUTHOR]

At work, three tasks were presented: road lane detection and trajectory estimation, environment mapping, and the application of a neural network. All these tasks are based on the results of the lane detection method. The presented lane detection method stands out due to the execution of an interpolation transformation for all previously detected edge points. This transformation transfers these points to a "bird's-eye" coordinate system and distributes them on a grid. Road lanes are identified by a lane feature filter based on the analysis of the distances between unique points. This allows lane views to be obtained in a coordinate system while preserving the distance condition. The road environment map is constructed from the obtained images using a probabilistic algorithm called Distributed Particle-SLAM (DP-SLAM). Based on the map result, a method for representing characteristic points describing the path of road lanes in each incoming camera image has been developed. These points are then used for training the neural network. The neural network solves a regression task for the coordinates of the points on the road lanes, enabling the identification of coefficients for parabolic fitting. Validation has been performed. [ABSTRACT FROM AUTHOR]

One of the main problems of modern big city is the global crisis of normal urban environment operation as a result of structural growing of automobilization level, oversaturation of road-street network by the transport streams. Buses of the especially large size class occupy, at the least, 20% in the structure of a modern bus park. The buses of especially large size classes, with the aim of necessary maneuverability, as a rule are made articulated. One of the ways of improving the maneuverability of articulated buses is the usage of guided towed sections. Solving the mentioned problem usually needs the mathematic models for predicting the vehicle behavior. The aim of introducing the trailer direction is to increase the overall traffic lane of an articulated bus that ensures a necessary maneuverability. The aim is gained by a choice of a rational transmitting ratio of trailer axle gear and necessary value of braking moment on one of its wheels. The solving of the problem concerning the stability of an articulated bus with a guided trailer axle was made by the way of analyzing the linear approaching of stationary vehicle movement. An influence of the construction parameters of the bus and the trailer, a transmitting ratio of control gear and a value of braking moment on the parameters of bus maneuverability were analyzed. It was proved that for an articulated bus, the normal maneuverability parameters can be ensured at the trailer direction by a way of braking of one of the wheels. [ABSTRACT FROM AUTHOR]

Introduction/purpose: The study of vehicle suspension is a challenge for researchers in the field of vehicles regarding the impact of the suspension system on vehicle performances such as ride comfort, road holding, and working space. This paper presents the simulation of the Land Rover Defender 110 vehicle in the roll plane (half vehicle) in Simulink/MATLAB. The obtained results were compared with the results obtained in the ADAMS/CAR software package of the Land Rover Defender 110 simulation model previously experimentally validated. The Defender 110 vehicle has a dependent suspension system in both axles and a passive suspension type with four degrees of freedom (4 DOF). Methods: The equations of the system can be solved mathematically with a scheme in Simulink/MATLAB while half-vehicle modeling has been done in ADAMS/CAR. Results: The comparison of the vehicle characteristics obtained by the two simulation methods was done for three different scenarios, and it was noticed that there is a good correlation between them. Conclusion: It was concluded that the Defender 110 vehicle simulation model in Simulink/MATLAB is validated. The validated model can be used to perform suspension system optimization in future work [ABSTRACT FROM AUTHOR]

This paper presents a new type of monorail rapid transit vehicle, which can operate in formation. In order to optimize the dynamic performance of the formation vehicles when passing through the small radius curve, this paper first analyzes the structure of the new monorail rapid transit vehicle, and establishes the vehicle dynamic model and multibody dynamics simulation model. Then the improved artificial potential field algorithm is used to build the formation vehicle operation controller. Finally, the dynamic performance evaluation index of the new monorail rapid transit formation vehicle is formulated, and the formation vehicle passing through the small radius curve scene is simulated. The simulation results show that the controller of the formation vehicle with dynamic optimization control can effectively optimize the load transfer coefficient of running wheels, unbalanced centrifugal acceleration and roll angle of vehicle body when passing through the curve. Through this control method, the dynamic performance of formation vehicles passing through small radius curve lines can be optimized. [ABSTRACT FROM AUTHOR]

Vehicle detection is crucial for traffic surveillance and assisted driving. To overcome the loss of efficiency, accuracy, and stability in low-light conditions, we propose a lightweight "You Only Look Once" (YOLO) detection model. A polarized self-attention-enhanced aggregation feature pyramid network is used to improve feature extraction and fusion in low-light scenarios, and enhanced "Swift" spatial pyramid pooling is used to reduce model parameters and enhance real-time nighttime detection. To address imbalanced low-light samples, we integrate an anchor mechanism with a focal loss to improve network stability and accuracy. Ablation experiments show the superior accuracy and real-time performance of our Light-YOLO model. Compared with EfficientNetv2-YOLOv5, Light-YOLO boosts mAP@0.5 and mAP@0.5:0.95 by 4.03 and 2.36%, respectively, cuts parameters by 44.37%, and increases recognition speed by 20.42%. Light-YOLO competes effectively with advanced lightweight networks and offers a solution for efficient nighttime vehicle-detection. [ABSTRACT FROM AUTHOR]

The large amounts of greenhouse gases generated in the logistics and transportation industry worsen global climate deterioration, jeopardizing the human living environment. A multi-objective nonlinear planning model for vehicle routing problems (VRPs) was established by considering VRPs with carbon emission constraints to realize the shortest vehicle mileage and the minimum carbon emission. An improved simulated annealing algorithm was proposed to solve the model. The farthest insertion heuristic method, combined with a relocation operator, was applied in the reinsertion operation to determine the preferred insertion back to the customer point and the optimal insertion point. The customer point with the largest minimum target increment was removed from the removed customer points and inserted into the optimal insertion point until all of them were inserted. Results demonstrate that the proposed algorithm outperforms the common genetic algorithms and forbidden search algorithms in similar studies with strong global optimization search capability. The improved algorithm could solve low-carbon VRP with high sensitivity and effectiveness. [ABSTRACT FROM AUTHOR]

The perception system is essential for autonomous vehicle safety and stability. However, on‐board sensors are vulnerable to external attacks, compromising driving strategies and security. Traditional attack detection methods, relying on static anomaly thresholds from data distributions, falter in complex driving scenarios. Recent studies have sought more nuanced detection techniques, including deep learning and inter‐vehicle communication, but these approaches face limitations related to data security and environmental dependency. This letter introduces an innovative, adaptive method for sensor attack detection in autonomous vehicles. By modelling the perception system's functions and employing the Gaussian process for probabilistic modelling, we generate dynamic uncertainty estimates that serve as adaptable anomaly boundaries. Our method's efficacy and resilience are validated through quantitative analysis of the real‐world KITTI dataset, demonstrating superior adaptive detection capabilities. [ABSTRACT FROM AUTHOR]

A microscopic traffic flow model is developed that incorporates vehicle vibrations due to pavement condition. The Intelligent Driver (ID) model employs a fixed exponent so traffic behavior is the same regardless of the road condition. Thus, it ignores the underlying physics. To address this limitation, the proposed model employs the Pavement Condition Index (PCI) in describing traffic behavior. The performance of both models is evaluated on a 3000 m circular road using the Euler numerical discretization technique. The results show that the performance of the proposed model varies with the pavement condition (PCI), as expected. Furthermore, the traffic flow increases with vehicle speed. The oscillations in speed and density with the proposed model decrease as the PCI increases, and are larger when the speed is higher. Consequently, the results with the proposed model align more closely with reality as they are based on the PCI, and so are a more accurate representation of traffic behavior. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles are certainly one of the key solutions for improving fuel efficiency and reducing emissions, especially in terms of special vehicles and with the use of CO2-neutral fuels. Determining the energy management strategy and finding the optimal solution with regard to the aforementioned goals remains one of the main challenges in the design of HEVs. This paper presents a new vehicle modeling method, with an emphasis on HEVs, which is based on the frequency analysis of emissions and consumption according to the current specific traction power of the vehicle. An evaluation of the newly introduced model in the RDE, NEDC and WLTP cycle was performed, and the results were compared with the standard verified vehicle model that was created in AVL's CruiseM R2021.2 software package. Positive traction energies have positive deviations of between 0.35% and 2.85%. The largest deviation in CO2 emissions was recorded for the HEV model in the RDE cycle and in the non-hybrid model in the WLTP cycle and were 3.79% and 4.4%, respectively. All other combinations of cycle and vehicles had deviations of up to about 1%. As expected, the largest relative deviations were recorded for NOx emissions and ranged from 0.13% to 9.62% for HEVs in the WLTP cycle. [ABSTRACT FROM AUTHOR]

The realistic simulation of the dynamic responses of a moving articulated vehicle has attracted considerable attention in various disciplines, with the identification of the vehicle model being the prerequisite. To this end, a double-sensor hump calibration method (DHCM) was developed to identify both unladen and laden vehicle models, consisting of a sensor layout optimization step and a system identification step. The first step was to optimize the number and position of sensors via parameter sensitivity analysis; the second was to inversely identify the vehicle system based on sensor responses. For comparison, the DHCM and the existing single-sensor hump calibration method (SHCM) were used to calibrate a small-sized vehicle model and a multi-axle articulated vehicle model. Vertical accelerations of the vehicle models were then simulated and characterized by power spectral densities (PSDs). Validation against experimental measurements indicated that the PSDs of the models identified with the DHCM matched the measured PSDs better than those of the SHCM, i.e., the DHCM-identified model accurately simulated the dynamic response of an articulated vehicle with relative errors below 16% in the low-frequency range. Therefore, the DHCM could identify models of small-sized vehicles and multi-axle articulated vehicles, while the SHCM was only suitable for the former. [ABSTRACT FROM AUTHOR]

Constantly tightening vehicle emission standards leads to the creation of new technologies for reducing the content of toxic substances in exhaust gases while maintaining the highest possible engine performance. At the moment, however, it is clear that with regard to future emission standards, despite the technologically highly refined system of catalytic converters, the reduction of emissions with the help of catalytic converters alone will be insufficient. That is why a variable exhaust manifold/system was developed and patented, which has a significant impact on the reduction of vehicle emissions. This innovation enables three working modes of the engine, depending on the demand and output power requirements. The presented article presents the complex architecture of the proposed system, including the description and simulation of individual operating modes. The first working mode is the most ecological and economical mode, but it has a relatively lower performance. The second working mode is characterized by the production of higher power, and the third mode can be used at high engine loads. [ABSTRACT FROM AUTHOR]

The high construction and operation costs of urban rail transit have prompted scholars to explore new urban rail transit systems and operating modes. Based on the new straddle-type rapid transit system proposed by our team, the collaborative control of intelligent formation vehicles is discussed in this paper. The mathematical formulation of straddle-type rapid transit vehicle (SRTV) operating in intelligent formation operation mode (IFOM) was first constructed, and then the formation vehicle safety protection strategy based on limited speed difference (LSD) was analyzed. Subsequently, based on the Model Predictive Control (MPC) algorithm, a formation collaborative operation controller was established for IFOM operation. Based on the typical operating scenarios of formation vehicles exiting, cruising, and entering the station, formation performance evaluation indicators were adopted, with a focus on optimizing the time for formation vehicles to exit and enter the station. The simulation results show that the formation operates well and the vehicles in the formation can safely and stably travel in formation, proving that the use of LSD speed limitation strategy and MPC formation controller can ensure the safety and stability of SRTV vehicle formation. [ABSTRACT FROM AUTHOR]

The recent widespread installation of renewable energy sources in place of fossil fuel, along with the substitution of electric vehicles, has given rise to several integration difficulties. Due to their intermittent nature, these systems are generally supplied through variable power generators. The control architecture of such connected systems focuses especially on minimizing frequency deviations and tie-line power fluctuations. In this paper, a cascaded proportional integral-proportional derivative (PI-PD) controller is proposed based on a chaotic butterfly optimization algorithm (CBOA) for parametric determination. The suggested controller and optimization approach is tested on a two-area power network, consisting of multiple renewable energy systems and electric vehicles while considering different load and frequency fluctuations. The proposed algorithm is evaluated based on four performance features. Its superiority is demonstrated by comparing it with recent optimization-algorithms-tuned controllers such as artificial rabbit algorithm 1 + PD, chaos game algorithm 1 + PI, dragonfly search algorithm, fractional order PID, and firefly algorithm PID. The acquired results validate the efficient performance of the proposed methodology by mitigating the frequency and tie-line power variations. Moreover, it exhibits robustness and enhanced stability of the system throughout a broad range of parameters. [ABSTRACT FROM AUTHOR]

In this paper the results of a simulation of a sports vehicle's braking maneuver in the adopted road conditions has been presented. The aim of this paper was to analyze the results and compare them with the ones obtained in the previous paper by the author, also prepared for the acceleration maneuver. At the same time considerations on the damaging effect of the road irregularities have been presented as this time no straightforward steering was included, so the vehicle could move freely. The simulated braking of the vehicle's model started at the speed of 100 km/h and its duration was 10 s. As in the previous paper the random road irregularities had three different amplitudes which enabled analysis of vehicle's deceleration on a different road conditions each time. Almost different profiles were also assumed for the left and the right wheels of the vehicle's model which moved on the dry and the icy road surface. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid growth in the number of electric vehicles (EVs), the large-scale grid connection of EVs has had a profound impact on the power grid. As a flexible energy storage resource, EVs can participate in auxiliary services of the power grid via vehicle-to-grid (V2G) technology. Due to the uncertainty of EVs accessing the grid, it is difficult to accurately control their charging and charging behaviors at both the day-ahead and real-time stages. Aiming at this problem, this paper proposes a two-stage scheduling strategy framework for EVs. In the presented framework, according to historical driving data, a day-ahead scheduling model based on distributionally robust optimization (DRO) is first established to determine the total power plan. In the real-time scheduling stage, a real-time scheduling model based on model predictive control (MPC) is established to track the day-ahead power plan. It can reduce the impact of EVs' uncertainties. This strategy can ensure the charging demand of users is under the control of the charging and discharging behaviors of EVs, which can improve the accuracy of controlling EVs. The case study shows that the scheduling strategy can achieve accurate and fast control of charging and discharging. At the same time, it can effectively contribute to the security and stability of grid operations. [ABSTRACT FROM AUTHOR]

Sharp steering-braking at a high speed exposes sport utility vehicles with high gravity centers and narrow wheel tracks to the risks of tire locking, sideslip and rollover. To avoid these risks and ensure braking safety, yaw stability and roll stability upon steering-braking, a braking-yaw-roll stability integrated control strategy was proposed, which consists of a supervisor, an upper and a lower controller for the front and rear axle independent drive electric vehicle. In the supervisor, a nonlinear vehicle predictive model was constructed and four control modes were proposed according to the vehicle status and rollover indexes. The weight coefficients between braking force, yaw stability and roll stability are determined dynamically by the control mode and output to the upper controller. The upper controller used a nonlinear model predictive control to determine the longitudinal braking force distribution of the four wheels. And in the lower controller, the regenerative braking torque and friction braking torque of each wheel were distributed. Finally, simulation verifications were carried out on the high and low adhesion roads. The results show that the control strategy proposed in this study can effectively prevent the vehicle from rollover while ensuring braking safety and yaw stability. [ABSTRACT FROM AUTHOR]

Vehicle and crew scheduling is vital in public transit planning. Conventionally, the issues are handled sequentially as the vehicle scheduling problem (VSP) and crew scheduling problem (CSP). However, integrating these planning steps offers additional flexibility, resulting in improved efficiency compared with sequential planning. Given the ever-growing market share of electric buses, this paper introduces a new model for integrated electric VSP and CSP, called EVCSPM. This model employs the minimum cost flow formulations for electric VSP, set partitioning for CSP, and linking constraints. Due to the nonlinear integer property of EVCSPM, we propose a method that hybrids a matching-based heuristic and integer linear programming solver, GUROBI. The numerical results demonstrate the efficiency of our methodology, and the integrated model outperforms the sequential model in real-life scenarios. [ABSTRACT FROM AUTHOR]

A significant resonance pattern at high frequencies around 400 Hz to 1000 Hz has been well observed from measured rail accelerations. Such resonances are key issues for rail noise and corrugation. Although this pattern has been noted and discussed in various papers in terms of wave reflections among multiple wheels, the aim of this study is to develop novel dynamic model and reveal generation mechanism of this resonance phenomenon with the train-track coupled system. An infinite Timoshenko beam with continuous supports is adopted for modeling the track system, and the point and transfer receptances of the rail for a moving excitation are explicitly deduced by the residue theorem together with a Fourier transform-based method. A frequency-domain method is then established to calculate the power spectral densities (PSD) of the train-track coupled system responses to stochastic irregularities with the moving vehicle model with multiple wheels. It is found that the high-frequency resonances occur when the reflected waves generated from multiple wheels on the rail are exactly in phase with the initial track irregularity. The load speed has negligible influence on the wheel–rail interaction in the lower frequency range and a small influence in the upper frequency range only for very high train speeds. [ABSTRACT FROM AUTHOR]

As a result of ever-growing energy demands, motor vehicles are among the largest contributors to overall energy consumption. This has led researchers to focus on fuel consumption, which has important implications for the environment, the economy, and geopolitical stability. This article presents a comprehensive analysis of various fuel consumption modeling methods, with the aim of identifying parameters that significantly influence fuel consumption. The scientific novelty of this article lies in its use of low-cost technology, i.e., an OBD-II interface paired with a mobile phone, combined with modern mathematical modeling methods to create an accurate model of the fuel consumption of a vehicle. A vehicle test drive was performed, during which variations in selected parameters were recorded. Based on the obtained data, a model of the vehicle's fuel consumption was built using three forecasting methods: a multivariate regression model, decision trees, and neural networks. The results show that the multivariate regression model obtained the lowest MSE, MAR, and MRSE coefficients, indicating that this was the best forecasting method among those tested. Sufficient forecast error results were obtained using neural networks, with increases of approximately 73%, 10%, and 131% in MSE, MAE, and MRAE, respectively, compared to regression results. The worst results were obtained with the decision tree model, with increases of approximately 163%, 21%, and 92% in MSE, MAE, and MRAE compared to the regression results. [ABSTRACT FROM AUTHOR]

Vehicle load estimation and health monitoring of bridges are of great importance for the health monitoring of bridge structure under vehicle loads. Traditional methods for the estimation of vehicle load require the positions of the vehicles. The vehicle position tracking is generally conducted in offline manner and requires the installation of additional sensors. To resolve these problems, we developed a Bayesian probabilistic approach for the online estimation of vehicle loads, vehicle positions, and structural parameters for bridges. The crux is to model the vehicle load vector as a modulated filtered Gaussian white noise due to the fact that the vehicle-bridge interaction forces are in essence the responses of the vehicle-bridge coupled system under the excitation of the road roughness described by Gaussian random field and the constant vehicle weights. Furthermore, the vehicle speed vector is introduced to track the unknown positions of vehicles. There are three appealing features in this approach. First, it allows the simultaneous estimation of vehicle loads, vehicle positions, and structural parameters in an online manner. Second, this method allows for time-varying vehicle speed tracking. Third, the proposed method is applicable to the case with multiple vehicles. Examples for the case where single/multiple vehicles pass across bridges with uniform speeds/variable speeds are presented to demonstrate the feasibility of the proposed method for vehicle load estimation, vehicle position tracking, and bridge structural identification using only strain measurements. [ABSTRACT FROM AUTHOR]

Model-based approaches to navigation, control, and fault detection that utilize precise nonlinear models of vehicle plant dynamics will enable more accurate control and navigation, assured autonomy, and more complex missions for such vehicles. This paper reports novel theoretical and experimental results addressing the problem of parameter estimation of plant and actuator models for underactuated underwater vehicles operating in 6 degrees-of-freedom (DOF) whose dynamics are modeled by finite-dimensional Newton-Euler equations. This paper reports the first theoretical approach and experimental validation to identify simultaneously plant-model parameters (parameters such as mass, added mass, hydrodynamic drag, and buoyancy) and control-actuator parameters (control-surface models and thruster models) in 6-DOF. Most previously reported studies on parameter identification assume that the control-actuator parameters are known a priori. Moreover, this paper reports the first proof of convergence of the parameter estimates to the true set of parameters for this class of vehicles under a persistence of excitation condition. The reported adaptive identification (AID) algorithm does not require instrumentation of 6-DOF vehicle acceleration, which is required by conventional approaches to parameter estimation such as least squares. Additionally, the reported AID algorithm is applicable under any arbitrary open-loop or closed-loop control law. We report simulation and experimental results for identifying the plant-model and control-actuator parameters for an L3 OceanServer Iver3 autonomous underwater vehicle. We believe this general approach to AID could be extended to apply to other classes of machines and other classes of marine, land, aerial, and space vehicles. [ABSTRACT FROM AUTHOR]

Braking control of urban rail vehicles with multiple carriages is critical to ensure the safe operation of urban rails. However, existing decentralized braking control methods lead to the inconsistent speed and excessive coupler force among carriages, which compromises the operation safety of urban rails. To address this issue, a cooperative braking strategy is proposed for urban rail vehicles based on Koopman model predictive control method. First, a cyber‐physical model is established, where the physical layer characterizes the dynamic model of multiple carriages using the Koopman operator. The cyber layer represents the communication topology of carriages with graph theory. Second, a cooperative braking controller is designed with the distributed model predictive control method. An optimisation problem is formulated to minimize the speed inconsistency and relative displacement difference among carriages. Third, extensive simulations under various scenarios, including normal and extreme operating conditions, are conducted to verify the effectiveness of the proposed method. Simulation results show that, compared with the classical decentralized method, the proposed braking method reduces the average relative displacement by 78% while reducing the speed error by 29%. [ABSTRACT FROM AUTHOR]

Current emission models primarily focus on traditional combustion vehicles and may not accurately represent emissions from the increasingly diverse vehicle fleet. The growing presence of hybrid and electric vehicles requires the development of accurate emission models to measure the emissions and energy consumption of these vehicles. This issue is particularly relevant for low-emission zones within cities, where effective mobility planning relies on simulation models using continuously updated databases. This research presents a two-dimensional emission model for hybrid vehicles, employing artificial neural networks for low-emission zones. The key outcome is the methodology developed to create a CO2 emission model tailored for hybrid vehicles, which can be used to simulate various road solutions. The CO2 emission model achieved an R2 coefficient of 0.73 and an MSE of 0.91, offering valuable information for further advancements in emission modelling. [ABSTRACT FROM AUTHOR]

This work proposes a centralized data-based orderly charging strategy that considers the user's charging choices. Three charging choices for different types of users are described. Then, a scheduling model of electric vehicles based on the time dimension is established. In this strategy, the optimization model not only considers the demand of the grid side and the user side, but also takes the driving data of electric vehicles as the driver. The grid-side optimization involves minimizing the equivalent load fluctuation, and the user-side is optimized to minimize the charging cost and maximize the charging electric quantity. The scheduling capabilities of the three charging strategies are analyzed based on a series of driving data of electric vehicles. The results show that the peak-valley difference and equivalent load fluctuation of the power grid in the data-based orderly charging strategy reduced by 22.2% and 22.7%, respectively, and the charging cost of users also reduced much more than the other two charging strategies. Additionally, the effect of users' charging choices on the charging strategy is analyzed, and the results show that the orderly charging strategy that considers users' charging choices can effectively decrease the scheduling deviation caused by users' charging choices. It greatly improves the security and economy of the grid. [ABSTRACT FROM AUTHOR]

Various statistical data indicate that mobile source pollutants have become a significant contributor to atmospheric environmental pollution, with vehicle tailpipe emissions being the primary contributor to these mobile source pollutants. The motion shadow generated by motor vehicles bears a visual resemblance to emitted black smoke, making this study primarily focused on the interference of motion shadows in the detection of black smoke vehicles. Initially, the YOLOv5s model is used to locate moving objects, including motor vehicles, motion shadows, and black smoke emissions. The extracted images of these moving objects are then processed using simple linear iterative clustering to obtain superpixel images of the three categories for model training. Finally, these superpixel images are fed into a lightweight MobileNetv3 network to build a black smoke vehicle detection model for recognition and classification. This study breaks away from the traditional approach of "detection first, then removal" to overcome shadow interference and instead employs a "segmentation-classification" approach, ingeniously addressing the coexistence of motion shadows and black smoke emissions. Experimental results show that the Y-MobileNetv3 model, which takes motion shadows into account, achieves an accuracy rate of 95.17%, a 4.73% improvement compared with the N-MobileNetv3 model (which does not consider motion shadows). Moreover, the average single-image inference time is only 7.3 ms. The superpixel segmentation algorithm effectively clusters similar pixels, facilitating the detection of trace amounts of black smoke emissions from motor vehicles. The Y-MobileNetv3 model not only improves the accuracy of black smoke vehicle recognition but also meets the real-time detection requirements. [ABSTRACT FROM AUTHOR]

In emergency scenarios, lane changing can provide a considerable advantage over braking by aiding in the prevention of rear-end collisions. However, executing lane changes on horizontal curves might lead to rollover collisions. This study proposes a systematic methodology for quantifying the rollover safety margin during lane-changing maneuvers by encompassing the complex characteristics of vehicle-road interactions. Specifically, an enhanced six-degree-of-freedom vehicle dynamics model was developed for a tractor-semitrailer and integrates road superelevation. Using this model, the rollover safety margin reduction rate (fS) was calculated. The fS represents the ratio of the difference between the lateral load transfer ratio margins under both reference state and emergency lane change conditions to the lateral load transfer ratio margin in the reference state. The reference state corresponds to vehicles maintaining 80 km·h-1 on a 270 m radius curve, while the emergency condition is defined as lane change durations of less than 4 seconds. The results reveal that emergency lane change maneuvers and roadway alignment significantly affect rollover safety margin. Shorter lane change duration, higher speed, and smaller radius worsen the rollover safety margin; these effects are further amplified when the lane change direction is opposite to the curve's bending direction. When the tractor-semitrailer performs a lane change at 60 km·h-1 within a 4-second duration on a 600 m radius curve, the fS exceeds 100%, indicating an imminent rollover. Consequently, this study contributes valuable evidence to the development of more reliable and secure lane-change strategies. [ABSTRACT FROM AUTHOR]

Track irregularity is the main factor that affects the safety and comfort of high-speed railway. It is important to study the influence of the track irregularity on the safety service of high speed train–track coupling system. In this paper, a vehicle–track spatial nonlinear coupled system dynamics model is developed with FEM. The model includes vehicle subsystem and track subsystem, which are coupled by wheel–rail interface. The vehicle subsystem is a 31-DOF spatial vehicle model, and the track subsystem is a three-dimensional slab track model composed of the rail, rail pad and track slab. Considering wheel tread profile and rail head profile, the wheel–rail spatial contact geometry relationship is established, and the "Trace method" and the "Minimum distance method" are used to search for wheel–rail contact points. Considering the nonlinear contact between the wheel and the rail, a numerical approach is presented, where the "Trace method" is embedded into the algorithm for solving the vehicle–track nonlinear coupling dynamics equation by cross iteration, realizing searching the wheel–rail contact points and solving the vehicle–track nonlinear coupling equation simultaneously, which greatly improves the efficiency of numerical analysis. As application examples, the dynamic responses of a high speed train–track spatial nonlinear coupled system excited by the track V-shaped local profile irregularity and the track torsional irregularity are analyzed. The results show that the algorithm is effective in solving the vehicle–track spatial nonlinear coupled system dynamics problem, and the track irregularity has significant influence on the dynamic response of vehicle and track structure. [ABSTRACT FROM AUTHOR]