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Planning for autonomous vehicles to merge into high‐density traffic flows within limited mileage is quite challenging. Specifically, the driving trajectory will inevitably have intersections with other vehicles whose driving intentions can't be directly observed. Herein, a two‐stage algorithm framework that is decomposed into the longitudinal and lateral planning processes for online merging planning is proposed. An improved particle filter is used to estimate the driving models of surrounding vehicles for predicting their future driving intentions. Based on Monte Carlo tree search (MCTS), different action spaces are evaluated for longitudinal merging gap selection and lateral interactive merging operation, while heuristic pruning is used to reduce the computation cost. Moreover, the coefficients related to the driving styles are introduced, and their influences on merging performance are analyzed. Finally, the proposed algorithm is implemented in a two‐lane simulation environment. The results show that the proposal has outperformed other baseline methods. [ABSTRACT FROM AUTHOR]

For rail-cum-road bridges with single-level arrangement, there are not only dynamic interference, but also aerodynamic interference between trains and vehicles, which means that the train-induced winds generated by high-speed trains are non-negligible for vehicles. In this work, the test method of moving vehicle model is used to test the aerodynamic characteristics of vehicles under train-induced winds. Then, the model of coupled vibration of train–vehicle–bridge system considering train-induced winds is established. Finally, taking a rail-cum-road bridge with single-level arrangement as an example, the coupled vibration of train–vehicle–bridge system considering train-induced winds is analyzed, and the influence of train-induced winds, lateral spacing between trains and vehicles on train–vehicle–bridge system is discussed. The results show that train-induced winds decrease with the increase of the lateral spacing between trains and vehicles. Without considering train-induced winds, the moving train has a greater impact on the vertical response of the vehicle, and a smaller impact on the lateral response of the vehicle. When considering train-induced winds, the lateral response of the vehicle increases significantly, but decreases with the increase of the lateral spacing. In addition, road conditions and the setting of anti-dazzle plates can change traffic safety under train-induced winds. [ABSTRACT FROM AUTHOR]

Vehicular safety is of considerable significance to the intelligent development of hybrid vehicles. However, the real-time stability control or reasonable torque distribution under the extreme road conditions remain a huge challenge due to the multiple uncertain parameters and difficulties to reconcile the handling and stability performance. To address the above problems for a through-the-road (TTR) 4-wheel-drive (4WD) hybrid vehicle, this study provides a handling and stability management (HSM) approach by incorporating the offline optimization rules and on-line model predictive control (MPC). Firstly, the vehicle dynamic model with seven degrees of freedom (7-DOF) is used to offline extract torque distribution rules (Offline-ETDR), and the online MPC feedback (Online-MPCF) is utilized to compensate the extra torque requirements for the poor effect under the extreme conditions. Accordingly, the offline optimization results and online correction are fused to provide the total torque demand given the real-time road condition detection. Finally, the real vehicle test are implemented to validate the effectiveness of the proposed torque coordination strategy. In comparison to the vehicle with no torque control strategy, the proposed method significantly improves the vehicle's cornering ability while also ensuring the high stability performance. [ABSTRACT FROM AUTHOR]

Regarding the issue of individual purchasing behavior in the rapid growth of electric vehicles, this article studies the diffusion model of electric vehicles considering individual choices and social effects from the perspective of the scale and quantity changes of electric vehicles. First, the neural network was used to predict the charging data of electric vehicles, and the economic effects of purchasing electric vehicles were calculated by combining the purchase cost and government subsidies. Then, the utility function for owners to purchase electric or traditional fuel vehicles was created by considering economic effects, cognitive attitudes, and social effects as factors that individuals need to consider when purchasing electric or traditional fuel vehicles. Finally, the discrete choice model was used to calculate the probability of users choosing to purchase electric or traditional fuel vehicles, and the number of electric vehicles was statistically calculated. Analysis of simulation examples shows that the growth rate of fuel vehicles decreases year by year during the simulation period, and the trend of electric vehicle growth follows an S-curve. [ABSTRACT FROM AUTHOR]

Deep learning algorithms have a wide range of applications, including cancer diagnosis, face and speech recognition, object recognition, etc. It is critical to protect these models since any changes to them can result in serious losses in a variety of ways. This article proposes the consortium blockchain-enabled conventional neural network (CBCNN), a four-layered paradigm for detecting malicious vehicles. Layer-1 is a convolutional neural network-enabled Internet-of-Things (IoT) model for the vehicle; Layer-2 is a spatial pyramid polling layer for the vehicle; Layer-3 is a fully connected layer for the vehicle; and Layer-4 is a consortium blockchain for the vehicle. The first three layers accurately identify the vehicles, while the final layer prevents any malicious attempts. The primary goal of the four-layered paradigm is to successfully identify malicious vehicles and mitigate the potential risks they pose using multi-label classification. Furthermore, the proposed CBCNN approach is employed to ensure tamper-proof protection against a parameter manipulation attack. The consortium blockchain employs a proof-of-luck mechanism, allowing vehicles to save energy while delivering accurate information about the vehicle's nature to the "vehicle management system." C++ coding is employed to implement the approach, and the ns-3.34 platform is used for simulation. The ns3-ai module is specifically utilized to detect anomalies in the Internet of Vehicles (IoVs). Finally, a comparative analysis is conducted between the proposed CBCNN approach and state-of-the-art methods. The results confirm that the proposed CBCNN approach outperforms competing methods in terms of malicious label detection, average accuracy, loss ratio, and cost reduction. [ABSTRACT FROM AUTHOR]

This study systematically assesses the efficacy of the vehicle scanning methods (VSM) in accurately estimating the mode shapes of bridges seated on elastic supports. Three state-of-the-art VSM methods are employed to obtain the mode shapes of bridges using the vehicle data during its travel. Two of the evaluated methods use a signal decomposition technique to extract the modal components of the bridge from the contact point of the response while the third one relies on the segmentation of the measured signals along the bridge deck and applying an operational modal analysis tool to each segmented signal to estimate the mode shapes. Numerical analyses are conducted on one single- and one two-span bridge, considering smooth and rough road profiles, different vehicle speeds, and presence of lead vehicle. The accuracy of the numerical models used in developing and assessing vehicle scanning models is tested, and the results of the study demonstrate the method using a half-car vehicle model and signal decomposition technique shows robustness against increasing vehicle speeds and road roughness while the method applying the segmentation of the measured signals provides relatively accurate mode shape estimates at the bridge edges at low speed, although the three methods have their limitations. It is also observed that simplified bridge and vehicle models can hide potential challenges that arise from the complexity of actual vehicle and bridge systems. Considering that a significant number of bridges worldwide are built on elastic supports, the practical success of vehicle scanning methods depends on their ability to handle elastic boundary conditions with reliability. Therefore, the article provides valuable insights into the capabilities and limitations of the current vehicle scanning methods, paving the way for further advancements and refinements in these techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a path-tracking method for agricultural vehicles based on the 4WIS-4WID structure and fuzzy control to improve the operation performance of agricultural machinery in the greenhouse. The influential model of two critical parameters, α and R, on the position correction is obtained based on the relationship analysis between the traditional pure pursuit model and the vehicle structure. Based on aiming pursuit, the relationship equation between the position deviation, lateral deviation d, and heading deviation θ is established. A two-input and two-output fuzzy controller is designed, and the lateral deviation d and heading deviation θ are the input variables. Values of α and R are obtained after fuzzification, fuzzy inference, and defuzzification, and a validated MATLAB model is used to simulate different scenarios. Results of the tests show that the steady-state error of path tracking based on fuzzy control pursuit is between 35 and 51 mm, and the stability distance is between 1661 and 3052 mm for straight path tracking in four initial states. The rectangular corners have the highest inaccuracy. The results of fuzzy control pursuit show a significant improvement in path-tracking performance that can influence vehicle navigation capability in the greenhouse. [ABSTRACT FROM AUTHOR]

Featured Application: The methodology presented in this paper can be applied to the design and evaluation of torque vectoring systems in hybrid electric vehicles. Comparison of different controllers is helpful in making design choices in these applications. In today's automotive industry, electrification is a major trend. In-wheel electric motors are among the most promising technologies yet to be fully developed. Indeed, the presence of multiple in-wheel motors acting as independent actuators allows for the implementation of innovative active systems and control strategies. This paper analyzes different design possibilities for a torque vectoring system applied to an originally compact front-wheel drive hybrid electric vehicle with one internal combustion engine for the front axle and two added electric motors integrated in the wheels of the rear axle. A 14 degrees of freedom vehicle model is present o accurately reproduce the nonlinearities of vehicle dynamic phenomena and exploited to obtain high-fidelity numerical simulation results. Different control methods are compared, a PID, an LQR, and four different sliding mode control strategies. All controllers achieve sufficiently good results in terms of lateral dynamics compared with the basic hybrid version. The various aspects and features of the different strategies are analyzed and discussed. Chattering reduction strategies are developed to improve the performance of sliding mode controllers. For a complete overview, control systems are compared using a performance factor that weighs control accuracy and effort in different driving maneuvers, i.e., ramp and step steering maneuvers performed under quite different conditions ranging up to the limits. [ABSTRACT FROM AUTHOR]

The advanced modeling and estimation of overall system dynamics play a vital role in electric vehicles (EVs), as the selection of components in the powertrain and prediction of performance are the key market qualifiers. The state-space averaged model and small-signal transfer function model are useful for assessing long-term behavior in system-level analysis and for designing the controller parameters, respectively. Both models take less computation time but ignore the high-frequency switching dynamics. Therefore, these two models could be impractical for the development and testing of EV prototypes. On the other hand, the multi-domain model in available simulation tools gives in-depth information about the short-term behavior and loss analysis of power electronic devices in each subsystem, considering the switching dynamics in a long computation time. In this paper, a general mathematical framework for the dynamical analysis of complete EVs is presented using a unified, switched nonlinear model. This equation-based model runs faster than the available module-based simulation tools. Two other models, namely the time domain state-space averaged model and frequency domain small-signal transfer function model, are also developed from the switched nonlinear model for the analysis with less computation time. The design and performance of an EV with two different motors and its controllers are evaluated using the general mathematical framework. [ABSTRACT FROM AUTHOR]

The construction industry faces a labor shortage problem, so construction vehicles need to be automated. For automation, a position estimation method is expected that is independent of the work environment and can accurately estimate the position of targets. This paper aims to develop a position estimation method for multiple construction vehicles using 3D LiDAR installed in a work environment. By focusing on the shape of construction vehicles, this method can estimate the location of construction vehicles in places where conventional methods cannot be used, such as valleys or roofs. Because the shape of the construction vehicle changes depending on the work equipment and steering operation, each joint angle was obtained, and the 3D model used for estimation was updated. As a result of the experiment, it was verified that the position and orientation of multiple construction vehicles can be estimated with an accuracy that satisfies the required accuracy. [ABSTRACT FROM AUTHOR]

Nowadays, the topic of reducing vehicles' energy consumption is very important. In particular, for electric vehicles, the reduction of energy consumption is necessary to remedy the most critical problems associated with this type of vehicle: the problem of the limited range of the electric traction, also associated with the long recharging times of the battery packs. To reduce use-phase impacts and energy consumptions of vehicles, it is useful to reduce the vehicle mass (lightweighting). The aim of this work is to analyze the parameters of a vehicle which influence the results of lightweighting, in order to provide guidelines for the creation of a vehicle model suitable for studying the effects of lightweighting. This study was carried out through two borderline case models, a compact car and an N1 vehicle, and simulating these through a consolidated vehicle simulation tool useful for consumption estimations. This study shows that the parameters that most influence the outcome of lightweighting are the rolling resistance, the battery pack characteristics, the aerodynamic coefficients, and the transmission efficiency, while the inertia contributions can be considered negligible. An analysis was also carried out with the variation of the driving cycle considered. [ABSTRACT FROM AUTHOR]

The motion planning and control method of automated vehicles, as the key technology of automated vehicles, directly affects the safety, comfort, and other technical indicators of vehicles. The planning module is responsible for generating a vehicle driving path. The control module is responsible for driving the vehicle. In this study, we review the main methods and achievements in motion planning and motion control for automated vehicles. The advantages and disadvantages of various planning and control methods are comparatively analyzed. Finally, some predictions and summaries based on the existing research results and trends are proposed. Through this analysis, it is believed that various types of algorithms will be further integrated in the future to complement each other's strengths and weaknesses. The next area of research will be to establish more accurate vehicle models to describe vehicle motion, improve the generalization-solving ability of algorithms, and enhance the planning and control of integrated 'human-vehicle-road' traffic systems. [ABSTRACT FROM AUTHOR]

This paper presents a state-of-the-art estimation technique by cross-combining a number n of filters for high-precision, reliable and robust vehicle sideslip angle state estimation, over a full range of vehicle operations irrespective of the driving mission and disruptions that may occur in the system. A machine-learning algorithm based on decision trees connects several filters together to switch between them according to the driving context, ensuring the best possible state estimate for relatively small and large sideslip angle values. In conjunction with the above-mentioned aspects, a seamless transition between different vehicle models is attained by observing the key parameters characterizing the lateral motion of the vehicle. The tests conducted using a prototype vehicle on a snow-covered track confirm the effectiveness and reliability of the proposed approach. [ABSTRACT FROM AUTHOR]

Passive suspensions cannot sufficiently reduce vehicle vibrations on tracks with poor irregularities or when the vehicle was operated at higher speeds. This study proposes three semi-active control algorithms for a secondary lateral damper to enable the vehicle to be operated on high-speed dedicated railways at 350 km/h and conventional low-speed railways at 160 km/h. A high-speed rail vehicle dynamics model with controllable dampers is built to study the feasibility of those algorithms through numerical investigations. Then, both field tests on track and lab tests on a full-scale roller rig were performed to examine the functionality of the controller hardware system and the valve-driven semi-active damper. Furtherly, a time-delay compensation based on the signal amplitude is developed and verified. Results from the on-track and roller rig tests state that the proposed semi-active control algorithms and the designed control system can effectively improve the ride quality of the vehicle when it is put into operation in different track conditions and at various speeds. [ABSTRACT FROM AUTHOR]

The detection of vehicle targets in wide-area Synthetic Aperture Radar (SAR) images is crucial for real-time reconnaissance tasks and the widespread application of remote sensing technology in military and civilian fields. However, existing detection methods often face difficulties in handling large-scale images and achieving high accuracy. In this study, we address the challenges of detecting vehicle targets in wide-area SAR images and propose a novel method that combines coarse-grained judgment with fine-grained detection to overcome these challenges. Our proposed vehicle detection model is based on YOLOv5, featuring a CAM attention module, CAM-FPN network, and decoupled detection head, and it is strengthened with background-assisted supervision and coarse-grained judgment. These various techniques not only improve the accuracy of the detection algorithms, but also enhance SAR image processing speed. We evaluate the performance of our model using the Wide-area SAR Vehicle Detection (WSVD) dataset. The results demonstrate that the proposed method achieves a high level of accuracy in identifying vehicle targets in wide-area SAR images. Our method effectively addresses the challenges of detecting vehicle targets in wide-area SAR images, and has the potential to significantly enhance real-time reconnaissance tasks and promote the widespread application of remote sensing technology in various fields. [ABSTRACT FROM AUTHOR]

In the future, considering the expansion of the autonomous driving society, autonomous driving systems that can drive safely and quickly will be required for the purpose of saving lives and transporting goods even on rough road such as snowy, icy, and unpaved roads. In such unknown environments, technologies that combine model-based control and artificial intelligence (AI) are attracting attention for the purpose of ensuring operational stability and reliability. The second author has proposed a vehicle driving model that is robust to road geometry and ever-changing environmental disturbances. This model is based on a two-wheel model, and expresses the error in the position of the center of gravity of the vehicle by the front wheel steering angle deviation, and adaptively estimates this deviation. However, this model has large modeling errors when driving at high velocity on slippery roads. In this study, we extend this model proposed in previous study, and propose a new vehicle driving model that can handle situations such as driving with large acceleration changes and steering on bad roads such as snowy and wet roads. Then, we demonstrate the usefulness of the proposed method in a simulation using vehicle motion analysis software. [ABSTRACT FROM AUTHOR]

The treatment and analysis of accidents involving heavy transport vehicles and pedestrians include the identification and treatment of a certain number of factors that may differ from the cases of passenger vehicle-pedestrian accidents. The aim of this paper is to develop a new model with better performance for speed estimation and reconstruction of accidents involving heavy vehicles and pedestrians. In a large number of cases during the research, it was observed that the experts used the same models for passenger vehicles as for transport vehicles. Likewise, a number of factors that have an impact on heavy vehicle accidents with pedestrians are not included as factors that have an impact on other accidents. The newly developed model, which has better performance than other models, can help experts in the case of analysis, speed determination, and reconstruction of accidents involving heavy vehicles and pedestrians. The model describes more than 94% of the most influential factors in the model (R2 = 0.945). This model will provide a novel way to examine crashes involving heavy vehicles and pedestrians, generating highly precise results for speed calculation which can be used to recreate the technical aspects of the accident. Additionally, it will help specialists in the field when preparing their expert opinion, specifically when heavy vehicles and pedestrians are involved, by providing a model which is different from the standard approach and yields more reliable outcomes. [ABSTRACT FROM AUTHOR]

In this paper, parallel navigation is proposed to track the target in three-dimensional space. Firstly, the polar kinematics models for the vehicle and the target are established. Secondly, parallel navigation is derived by using polar kinematics models. Thirdly, cell decomposition method is applied to implement obstacle avoidance. Fourthly, a brief study is given on the inuence of uncertainties. Finally, simulations are conducted by MATLAB. Simulation results demonstrate the effectiveness of the parallel navigation. [ABSTRACT FROM AUTHOR]

Based on the analysis of the operating conditions of the tractor, a Hybrid four-wheel drive tractor is proposed, and formulate the torque distribution control strategy based on fuzzy control, to control the driving wheel slip rate of the Hybrid four-wheel drive tractor in the high traction efficiency operating range of the tractor. The vehicle model of the Hybrid four-wheel drive tractor is established in AVL-CRUISE software, and the torque distribution control strategy based on fuzzy control is established in MATLAB/Simulink software. The AVL-CRUISE and MATLAB/Simulink co-simulation was carried out based on the plowing condition of the tractor. The simulation results show that the torque distribution control strategy based on fuzzy control can control the driving wheel slip rate of the Hybrid four-wheel drive tractor in the high traction efficiency operating range, the power performance of the Hybrid four-wheel drive tractor is improved, while the engine runs smoothly and is always in the high-efficiency range of engine operation, and the economy is better. [ABSTRACT FROM AUTHOR]

In this paper, we present the adaptation of the terminal component learning-based model predictive control (TC-LMPC) architecture for autonomous racing to the Formula Student Driverless (FSD) context. We test the TC-LMPC architecture, a reference-free controller that is able to learn from previous iterations by building an appropriate terminal safe set and terminal cost from collected trajectories and input sequences, in a vehicle simulator dedicated to the FSD competition. One major problem in autonomous racing is the difficulty in obtaining accurate highly nonlinear vehicle models that cover the entire performance envelope. This is more severe as the controller pushes for incrementally more aggressive behavior. To address this problem, we use offline and online measurements and machine learning (ML) techniques for the online adaptation of the vehicle model. We test two sparse Gaussian process regression (GPR) approximations for model learning. The novelty in the model learning segment is the use of a selection method for the initial training dataset that maximizes the information gain criterion. The TC-LMPC with model learning achieves a 5.9 s reduction (3%) in the total 10-lap FSD race time. [ABSTRACT FROM AUTHOR]

The dual-carbon strategy advocates a green, environmentally friendly, and low-carbon lifestyle. In the field of transportation, electric vehicles (EVs) have been regarded as an effective solution to reduce carbon emissions and to conserve energy. Developing a reasonable charging guidance scheme for users is a feasible way to solve problems, such as the range anxiety of EV users, and has a great application value for the promotion of EVs in the future. In practical situations, how to develop charging induction schemes for users that better meet their needs according to the type of user and their multi-dimensional preferences is the focus of this paper. To this end, this study utilized charging behavioral data to investigate the multi-dimensional charging preference of users based on the collaborative filtering algorithm. Then, a multi-objective optimization model was established based on the preference degree of each charging station and the integrated travel cost. An NSGA-III framework was used to design the algorithm to solve the proposed model. The algorithm was tested using simulation experiments that were designed based on the road network and charging stations in Beijing. The final result is an experimental analysis of the weight matrices for the three different preferences of minimum energy consumption cost, minimum time cost, and minimum fee cost, which yields a difference of about 4.4% between the optimal energy consumption cost and the maximum energy cost, about 2.9% between the optimal time cost and the maximum time cost, and about 10% between the optimal fee cost and the maximum fee cost under these three different preferences, respectively. The proposed multi-objective optimization model is able to provide users with reliable charging station selection by incorporating their personalized charging preference characteristics and charge guidance schemes. [ABSTRACT FROM AUTHOR]

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This paper studies how autonomous vehicles (AVs) influence future traffic and energy consumption when adopting various route guidance strategies, which can be divided into global information strategies and local information strategies according to the scope of information collection. A mixed traffic flow model is established. Then, an autonomous vehicle model is improved to investigate the impact of six route guidance strategies. The selected strategies are specifically evaluated in a double-route scenario, considering both single-exit and dual-exit configurations. Three indicators are chosen to evaluate traffic efficiency, including traffic flow, average speed and quantity of vehicles. Consumption per unit flux is the indicator of energy consumption level. Simulation results show that autonomous vehicles can improve traffic efficiency and reduce energy consumption. [ABSTRACT FROM AUTHOR]

Two stages of halal food distribution model for perishable food products are a mixed integer linear program (MILP) model proposed to solve the distribution problem of halal food, especially for perishable food products. The model can simultaneously minimize overstock, shortage, transportation, and deterioration costs. The model is developed into two stages. The first stage is the location-allocation model to determine the halal cluster and the number of suppliers in each cluster. The second stage is the vehicle routing model to determine the routing at each cluster. Numerical experiments are done using CPLEX Solver and the proposed model is applied to solve a real case of halal meat distribution in Yogyakarta. The results show that the proposed model can be used as a decision tool for supply chain and distribution managers to determine the strategy for distributing halal food products with the least total logistics cost for daily application. [ABSTRACT FROM AUTHOR]

Road accidents are one of the major cause of human deaths around the globe. In order to accelerate autonomous driving, a problem of designing suitable lateral and longitudinal drive control scheme, with an obstacle avoidance mechanism has been considered. For this purpose, a two-wheel equivalent model of a vehicle has been adopted and steering angle velocity is treated as a control input. A cascaded architecture for Obstacle Avoidance and Drive Control has been presented. Impact point algorithm has been developed for Obstacle Avoidance System that receives data from various onboard sensors, and computes potential impact probability based on position and velocity of own vehicle and potential static and moving obstacles. The reference heading and speed signals are continuously updated by the Obstacle Avoidance System. Feedback linearization based lateral and longitudinal drive control laws have been proposed to control the required heading angle and longitudinal speed, respectively. Performance of the proposed control scheme for various operational scenarios has been evaluated through simulations. [ABSTRACT FROM AUTHOR]

Solving the stability region in the plane motion of vehicles has become a hot research topic in vehicle handling stability under extreme conditions, but there is still a lack of research on the stability region under steering and braking conditions. In this paper, a five-degree-of-freedom (5DOF) nonlinear dynamic model of a vehicle with braking torque introduced is established, and the model is transformed into an equivalent system by using the D'Alembert principle. Then, the equilibrium points of the equivalent system are solved by using an improved hybrid algorithm combining the genetic algorithm (GA) and sequential quadratic programming (SQP) method. According to the bifurcation characteristics of the equilibrium points, the boundary of the stability region at the given initial longitudinal velocity is determined, and the three-dimensional stability region is fitted. Finally, the stability region of the equivalent system and the original system are analyzed by the energy dissipation method, and the stability region determined by the equilibrium point bifurcation method is verified by simulation. The results show that as the braking torque increases, the number of equilibrium points increase to three from one, the equilibrium bifurcation method proposed in this paper can effectively solve the stability region of the equivalent system, and the solution results are consistent with the original system stability region. When the limited braking torque is 500 N·m and the initial longitudinal velocity increases from 30 m/s to 50 m/s, the absolute value of the front wheel steering angle at the boundary point changes from less than 0.02 rad to more than 0.02 rad. [ABSTRACT FROM AUTHOR]

Given the lack of systematic research on bathymetric surveys with multi-beam sonar carried by autonomous underwater vehicles (AUVs) in unfamiliar waters, this paper proposes a method for multi-beam bathymetric surveys based on the constant-depth mode of AUVs, considering equipment safety, operational efficiency, and data quality. Firstly, basic principles for multi-beam bathymetric surveys under the constant-depth mode are proposed based on multi-beam operational standards and AUV constant-depth mode characteristics. Secondly, a vertical effective height model for the vehicle is established, providing vertical constraints and a basis for determining fixed depth in constant-depth missions. Subsequently, according to these basic principles and the vertical effective height model, the operational process for multi-beam bathymetric surveys in unfamiliar waters under the AUV constant-depth mode is outlined. Finally, we validate the proposed method through sea trials in the Xisha Sea of the South China Sea. The test results show that the method proposed in this paper not only ensures the vehicle safety operation and multi-beam data quality, but also improves the operation efficiency by about 68%, demonstrating the reliability of the proposed method and its significant engineering value and guidance implications. [ABSTRACT FROM AUTHOR]

This article presents a topology optimization of the motor mounting bracket in a 2-degreeof-freedom (2 DoF) vehicle simulator that is enhanced by the driving scenarios. Firstly, a 14 DoF passenger reference application model is determined in the Simulink environment. Then, common driving scenarios (Constant Radius, Double Lane Change, Fishhook, Increasing Steer, Sine with Dwell and Swept Sine) are run on 14 DoF vehicle models to test the dynamic performance of the vehicle. During the analysis, accelerations in the XYZ axes are logged, and the minimum and maximum acceleration values on each axis are grouped separately for each driving scenario. Next, the concept design of 2 DoF vehicle simulators is created. The obtained accelerations from the driving scenarios are then run on 2 DoF vehicle simulator in the Solidworks simulation environment, and stress and deformation on the 2 DoF vehicle simulator are analyzed. During this analysis, linear actuator and axis forces are calculated according to the reaction forces on the vehicle simulator. Under the determined axial forces, the brackets are subjected to topology optimization. The obtained generative design of the bracket is reshaped by post-processing for sustainable production. The shape-optimized bracket is run again on the 2 DoF vehicle simulator with the obtained acceleration values from the driving scenarios, and the study is completed by performing stress and deformation analysis. [ABSTRACT FROM AUTHOR]

Scanning the bridge's frequencies from the passing vehicle's vibration data has been frequently investigated recently. However, in previous studies, vehicles were typically simplified to quarter- or half-car models, and apparent disparity could be observed between the models and real vehicles. To make the vehicle model more practical, in this study, a 3D vehicle model is built to extract the bridge's frequencies from vehicle vibrations. For the first time, equations for calculating the contact-point (CP) response of the 3D vehicle model are derived with tire damping. Furthermore, residual CP responses between front and rear wheels are utilized to eliminate the inverse effects of road roughness, making the bridge frequencies outstanding in the frequency domain. The robustness of the proposed method is tested under different influence factors, and two possible measurement errors are as follows: the sensor position and axle distance when applying the proposed method in engineering. Results show that the proposed method performs stably under the influence of different road roughness classes and tire damping. Bridge frequencies can be identified when the vehicle is travelling at a highway speed (108 km/h in this study). Environmental noises can submerge the bridge's high-order frequencies but have little influence on the low-frequency range. High bridge damping will restrain the transmission of bridge vibration to the vehicle, making high-order bridge frequencies less visible. In addition, the errors introduced by a vehicle body sensor position can be eliminated when calculating the CP responses for tires, thus will not influence bridge frequency identification. To avoid possible errors induced by manual measurement of the axle distance, a novel cross-correlation function-based method is employed, which is verified effective and practical for calculating residual CP responses. [ABSTRACT FROM AUTHOR]

This study proposes a two-level optimization model system for vehicle control and signal timing at isolated signal intersections under the mixed traffic flow environment composed of intelligent connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), to minimize the energy consumption and vehicle delay at intersections. The proposed two-layer optimization model is composed of a two-layer vehicle trajectory control model and a fuzzy control signal timing optimization model. The two-layer vehicle trajectory control model includes a signal-oriented vehicle trajectory control model and a car-following oriented vehicle trajectory control model. The former calculates expected acceleration and speed commands at each time step according to the coming signal information, to help vehicle pass the closest signal intersection without stopping during the green light interval; the latter uses the variable headway (VTH) strategy to follow the preceding vehicle by maintaining a safe distance. A microscopic simulator based on SUMO is developed to test the performance of the proposed optimization algorithm. In the simulation experiment, with the driving characteristics of CHV drivers considered, the results show that our model performs well under a CAV penetration rate of 30%–60% and under small or moderate levels of traffic flow. The average waiting time of vehicles is reduced by about 25% compared with the uncontrolled scheme. Under the condition of penetration rate of 60%, the average energy consumption of vehicles in the proposed model is 17.56% lower than that of the uncontrolled scheme. In addition, the proposed model reduces by 21.94% compared with the scheme of only controlling vehicles. When the traffic flow is at a low or medium level, the average energy consumption and waiting time of vehicles are reduced by nearly 35% with the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, we present a study of the license plate recognition (LPR) system for Taiwanese vehicles using a cascade of You Only Look Once version 4 (YOLOv4) detectors. The LPR system is composed of a vehicle detection model, a license plate (LP) detection model, an LP corner prediction model, and an LPR model. Herein, the pretrained YOLOv4 model was directly applied to vehicle detection. The YOLOv4 framework was adopted in the LP detection and LP recognition models, performing transfer learning on each model. Furthermore, to enhance the accuracy of the LPR system, an LP corner prediction model was developed in this study to predict the four corner positions of an LP to perform a perspective transformation on the plate for alignment purposes. The experimental results show that our LPR system achieves an accuracy of 98.88% when tested on 2049 images of the application-oriented LP dataset, outperforming most LPR systems reported in the literature. [ABSTRACT FROM AUTHOR]

Autonomous driving is an important branch of artificial intelligence, and real-time and accurate object detection is key to ensuring the safe and stable operation of autonomous vehicles. To this end, this paper proposes a fast and accurate object detector for autonomous driving based on improved YOLOv5. First, the YOLOv5 algorithm is improved by using structural re-parameterization (Rep), enhancing the accuracy and speed of the model through training-inference decoupling. Additionally, the neural architecture search method is introduced to cut redundant branches in the multi-branch re-parameterization module during the training phase, which ameliorates the training efficiency and accuracy. Finally, a small object detection layer is added to the network and the coordinate attention mechanism is added to all detection layers to improve the recognition rate of the model for small vehicles and pedestrians. The experimental results show that the detection accuracy of the proposed method on the KITTI dataset reaches 96.1%, and the FPS reaches 202, which is superior to many current mainstream algorithms and effectively improves the accuracy and real-time performance of unmanned driving object detection. [ABSTRACT FROM AUTHOR]

Advanced active safety systems play a crucial role in ensuring the safe driving of vehicles in critical conditions such as an obstacle avoidance manoeuvre. However, conventional techniques relying mainly on braking interventions may not result in the desired vehicle response in such situations. Over-actuation through the control of individual motion actuators could potentially improve the safety performance of vehicles. This study evaluates various configurations of motion actuators for path following and yaw stability control of vehicles in critical driving scenarios. The configurations include active front steering (S), active front steering + torque vectoring (ST), active front steering + active camber (SC) and active front steering + torque vectoring + active camber (STC). The evaluation is achieved based on a nonlinear model predictive control formulation, which considers yaw stability and the physical limits of motion actuators. This problem formulation uses a double-track vehicle model, combined with the Dugoff tyre model and its variant with the camber effect, to model the vehicle dynamics. The actuator configurations are evaluated regarding the passing velocity, tracking accuracy, safety distance and robustness to reference trajectory variation. The results indicate that the integrated control of STC performs the best among all the four configurations while S performs the worst. Furthermore, SC is generally superior to ST. [ABSTRACT FROM AUTHOR]

This work is focused on the simulation of a complete hybrid bus vehicle model performing a real-world driving cycle. The simulation framework consists of a coupled co-simulation environment, where all the vehicle sub-system models are linked to achieve a real time exchange of input and output signals. In the vehicle model also the electric devices of the powertrain and accumulation system are included. This co-simulation platform is applied to investigate the hybridization of a 12-m city bus, performing a typical urban driving mission. A comparison between the conventional powertrain is performed against the hybridized version, to highlight the advantages and challenges. In particular, the novelty of this modeling approach is that the IC engine simulation does not rely on pre-processed look-up tables, but exploits a high-fidelity one-dimensional thermo-fluid dynamic model. However, it was necessary to develop a fast simulation methodology to exploit this predictive tool, achieving a low computational cost. The 1D engine model is first validated against the experimental engine map data available, showing a good model predictivity. Then the 1D engine model and the other models of the powertrain are coupled to the vehicle model, in order to follow the prescribed velocity profile of the driving cycle. The complete model is applied under different conditions, to evaluate the impact on performance and emissions and assess the simulation predictivity. [ABSTRACT FROM AUTHOR]

This paper presents a method for the identification of control-related signal paths dedicated to a semi-active suspension with MR (magnetorheological) dampers, which are installed in place of standard shock absorbers. The main challenge comes from the fact that the semi-active suspension needs to be simultaneously subjected to road-induced excitation and electric currents supplied to the suspension MR dampers, while a response signal needs to be decomposed into road-related and control-related components. During experiments, the front wheels of an all-terrain vehicle were subjected to sinusoidal vibration excitation at a frequency equal to 12 Hz using a dedicated diagnostic station and specialised mechanical exciters. The harmonic type of road-related excitation allowed for its straightforward filtering from identification signals. Additionally, front suspension MR dampers were controlled using a wideband random signal with a 25 Hz bandwidth, different realisations, and several configurations, which differed in the average values and deviations of control currents. The simultaneous control of the right and left suspension MR dampers made it necessary to decompose the vehicle vibration response, i.e., the front vehicle body acceleration signal, into components related to the forces generated by different MR dampers. Measurement signals used for identification were taken from numerous sensors available in the vehicle, e.g., accelerometers, suspension force and deflection sensors, and sensors of electric currents, which control the instantaneous damping parameters of MR dampers. The final identification was carried out for control-related models evaluated in the frequency domain and revealed several resonances of the vehicle response and their dependence on the configurations of control currents. In addition, the parameters of the vehicle model with MR dampers and the diagnostic station were estimated based on the identification results. The analysis of the simulation results of the implemented vehicle model carried out in the frequency domain showed the influence of the vehicle load on the absolute values and phase shifts of control-related signal paths. The potential future application of the identified models lies in the synthesis and implementation of adaptive suspension control algorithms such as FxLMS (filtered-x least mean square). Adaptive vehicle suspensions are especially preferred for their ability to quickly adapt to varying road conditions and vehicle parameters. [ABSTRACT FROM AUTHOR]

This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, including wheeled tractors, tracked tractors, rice transplanters, high clearance sprays, agricultural robots, agricultural tractor–trailers, etc. The application and improvement of path-tracking control methods are summarized based on the different working scenes and types of agricultural machinery. This study explores each of these methods in terms of accuracy, stability, robustness, and disadvantages/advantages. The main challenges in the field of agricultural vehicle path tracking control are defined, and future research directions are offered based on critical reviews. This review aims to provide a reference for determining which controllers to use in path-tracking control development for an autonomous agricultural vehicle. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the control of differential steering for four-in-wheel-motor electric vehicles. The so-called differential steering means that the front wheel steering is realized through the differential driving torque between the left and right front wheels. With the consideration of tire friction circle, a hierarchical control method is proposed to realize the differential steering and the constant longitudinal speed simultaneously. Firstly, the dynamic models of the front wheel differential steering vehicle, the front wheel differential steering system and the reference vehicle are established. Secondly, the hierarchical controller is designed. The upper controller is to obtain the resultant forces and resultant torque required by the front wheel differential steering vehicle tracking the reference model through the sliding mode controller. In the middle controller, the minimum tire load ratio is selected as the objective function. Combined with the constraints, the resultant forces and resultant torque are decomposed into the longitudinal and lateral forces of four wheels by the quadratic programming method. The lower controller provides the required longitudinal forces and tire sideslip angles for the front wheel differential steering vehicle model through the tire inverse model and the longitudinal force superposition scheme. Simulation results show that the hierarchical controller can guarantee the vehicle to track the reference model well on both of the high and low adhesion coefficient road with all of the tire load ratios smaller than 1. It can be drawn that the control strategy proposed in this paper is effective. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) techniques implemented at a large scale in intelligent transport systems (ITS), have considerably enhanced the vehicles' autonomous behaviour in making independent decisions about cyber threats, attacks, and faults. While, AI techniques are based on data sharing among the vehicles, it is important to note that sensitive data cannot be shared. Thus, federated learning (FL) has been implemented to protect privacy in vehicles. On the other hand, the integrity of data and the safety of aggregation are ensured by using blockchain technology. This paper applied classification approaches to VANET and ITS cyber‐threats detection at the vehicle. Subsequently, by using blockchain and by applying an aggregation strategy to different models, models from the previous step were uploaded in a smart contract. Lastly, we returned the updated models to the vehicles. Furthermore, we conducted an experimental study to measure the effectiveness of the proposed prototype. In this paper, the VeReMi data set was distributed in a balanced manner into five parts in the experimental study. Thus, classification techniques were executed by each vehicle separately, and models were generated. Upon the aggregation of the models in blockchain, they were returned to the vehicles. Lastly, the vehicles updated their decision functions and accessed the precision and accuracy of cyber‐threat detection. The results indicated that the precision and accuracy decreased by 7.1% on average with comparable F1‐score and recall. Our solution ensures the privacy preservation of vehicles whereas blockchain guarantees the safety of aggregation technique and low gas consumption. [ABSTRACT FROM AUTHOR]

The vehicle particle model was built to compare and analyze the effectiveness of three different collision avoidance methods. The results show that during vehicle high-speed emergency collision avoidance, lane change collision avoidance requires a smaller longitudinal distance than braking collision avoidance and is closer to that with a combination of lane change and braking collision avoidance. Based on the above, a double-layer control strategy is proposed to avoid collision when vehicles change lanes at high speed. The quintic polynomial is chosen as the reference path after comparing and analyzing three polynomial reference trajectories. The multiobjective optimized model predictive control is used to track the lateral displacement, and the optimization objective is to minimize the lateral position deviation, yaw rate tracking deviation, and control increment. The lower longitudinal speed tracking control strategy is to control the vehicle drive system and brake system to track the expected speed. Finally, the lane changing conditions and other speed conditions of the vehicle at 120 km/h are verified. The results show that the control strategy can track the longitudinal and lateral trajectories well and achieve effective lane change and collision avoidance. [ABSTRACT FROM AUTHOR]

The intelligent tracing car is a complex system with high linearity, and the design of path tracking strategy is one of the most important technologies in intelligent tracing car field. This paper proposed a novel path tracking strategy based on control dynamic model of the intelligent tracing car. The vehicle dynamic models are established separately according to the vehicle parameters and kinetic equation. The traditional path tracking strategy used the distance between the desired and actual displacements to compute the error for position control. However, the paper utilized the past and current states of the desired displacement to figure out a motion vector. The error was acquired through calculating the difference between the desired and current displacements along this motion vector. Simulation results showed excellent performance in position control and speed control for the intelligent tracing car. [ABSTRACT FROM AUTHOR]

• DRLs are designed to be on during the day to increase the visibility of vehicles. • There have been no recent studies evaluating DRLs currently present in the fleet. • DRLs reduce non-night-time multi-vehicle crash involvement by 8.8% • Crash reductions were higher at dawn or dusk and in higher speed zones. • A DRL mandate would likely lead to reductions in the overall crash risk of the fleet. Introduction : Many studies have found that daytime running lights (DRLS) are effective in reducing daytime multi-vehicle crashes. From an Australian perspective, while studies exist using data from other jurisdictions, there has been uncertainty about how effective DRLs would be under Australian environmental conditions, which can differ from other parts of the world. In addition, in recent years DRLs have become a standard feature of many new vehicles. The objective of this work was to utilize Australian crash data to estimate the impact of DRLs on casualty crash risk reflecting the Australian crash population and local conditions. It also aimed to broadly examine the real-world crash-based effectiveness of DRLs currently present in the light vehicle fleet. Method : The study utilized police reported casualty crash data for crashes that occurred during 2010–2017. The analysis used induced exposure methods, which offers the potential to assess the relationship between crash risk and DRL fitment by intrinsically controlling for confounding factors. Results : It was found that DRL fitment can reduce the overall risk of being involved in a non-nighttime multi-vehicle crash where vehicle visibility may be a factor in crash causation by a statistically significant 8.8%. Estimated crash reductions were higher at dawn or dusk and in higher speed zones. Conclusion : Results provide clear evidence that mandating DRLs on all new vehicles would likely lead to reductions in the overall crash risk of the fleet through accelerating fitment through the fleet. Practical Application : DRL fitment can reduce the overall risk of being involved in a non-night-time multi-vehicle crash where vehicle visibility may be a factor in crash causation. Governments should consider a DRL mandate on all new vehicle models, including all variants to accelerate the process of fitment through the fleet. This would likely lead to reductions in the overall crash risk of the fleet. [ABSTRACT FROM AUTHOR]

Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world model for vehicle detection and classification from single-beam LiDAR of a roadside parking scenario. The paper presents a synthetically augmented transfer learning approach for LiDAR-based vehicle detection and the implementation of synthetic LiDAR data. A synthetic augmented transfer learning method was used to supplement the small real-world data set and allow the development of data-handling techniques. In addition, adding the synthetically augmented transfer learning method increases the robustness and overall accuracy of the model. Experiments show that the method can be used for fast deployment of the model for vehicle detection using a LIDAR sensor. [ABSTRACT FROM AUTHOR]

The irregular ocean environment brings difficulties to observation and affects the movement state of underwater vehicles. Multisource information fusion can provide more reliable results on information-rich datasets. This paper proposes a multisource information fusion-based environment perception and dynamics model for underwater vehicles in irregular ocean environments. First, the multisource observation data is decomposed by the latent feature disentanglement learning model into the latent stable and dynamical features. The dynamical feature is predicted by the retrospect regression method and then integrated with the latent stable feature to predict the ocean field. After that, the environment-vehicle coupling effect is explored by thermodynamics and statics simulation. Then, a real-time dynamic model of the underwater vehicle is constructed by combining multisource information from physical principles, environmental perception, and sensor observations. Finally, extensive experiments are performed on a novel underwater glider and a publicly available South China Sea dataset to verify the proposed method. • An environment perception model is proposed for the underwater vehicle array. • A real-time dynamic model is proposed for the underwater vehicle. • Environment-vehicle coupling effect is explored. [ABSTRACT FROM AUTHOR]

The automotive industry is experiencing increasing competition, and vehicle development is becoming increasingly complex. Manufacturers must therefore be able to rapidly compare the outcomes of experimental tests carried out under different conditions. Robust simulation tools that can adjust for external factors have the potential to save a significant amount of time. In this regard, the purpose of this paper is to propose a method for evaluating the effect of asphalt temperature on tire and vehicle lateral dynamic performance, based on empirical data. Because rubber is a viscoelastic material, its properties are heavily influenced by the operating conditions. Therefore, a corrective algorithm must be created to enable the transfer of results obtained from tests carried out under different asphalt temperature conditions to a reference temperature of 25 °C. This article presents an analytical model that accurately describes this phenomenon, as well as the methods employed to generalize and optimize the model. Generalizability represents a crucial aspect of this research, as the model must be widely applicable across several vehicle categories while requiring minimal data to perform the corrections effectively. Finally, the analytical compensatory tool was incorporated into a MATLAB bicycle model to update the numerical transfer function measurements that describe the vehicle's dynamic behavior during experimental maneuvers. These results indicate that modest data is needed to achieve good levels of accuracy, making the model and vehicle dynamics implementation promising. [ABSTRACT FROM AUTHOR]

This study combines laboratory experiments and numerical modelling in a novel manner to assess vehicle stability. Assessing vehicle stability forms the basis of hazard classification criteria, which in turn helps in forming safety guidelines for stream crossings and planning of evacuation routes in times of floods. These criteria are based on theoretical and physical model studies carried out on different vehicle models. This article demonstrates the application of a numerical method to determine the vehicle stability threshold so that the need for a physical model study for each kind of vehicle may be avoided. The numerical investigation is performed using smoothed particle hydrodynamics (SPH) with the vehicle oriented perpendicular to the flow direction, as this is the most critical orientation. A physical model study is also performed and its results are used to validate the SPH model. The results confirm the current Australian Rainfall and Runoff (ARR) safety criteria for stationary vehicles. It also suggests that the ARR stability curve can shift depending on the road conditions that affect the vehicle's sliding mechanism. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach to dynamic wireless charging that allows electric vehicles to charge wirelessly while in motion in both lanes on highways. The challenge is to locate the charging infrastructure on a highway between origin O and destination S (round trip) with heterogeneous battery vehicles, where each type of vehicle requires its allocation of charging segments on the road. We aim to ensure that each type of vehicle can complete a round trip without running out of battery charge while minimizing the number of charging segments and inverters on the road by studying both lanes simultaneously. We model the problem mathematically and validate it using a CPLEX optimizer for limited instances. Finally, we solve the problem using a hybrid approach that combines genetic algorithms and local search techniques to balance diversification and intensification. We have significantly improved the results found in the literature by reducing the number of inverters, which are expensive components in the charging infrastructure. Our approach takes advantage of utilizing a single inverter for both lanes of the highway, leading to cost savings and improved efficiency. [ABSTRACT FROM AUTHOR]

To deal with the threat from flying vehicles, it is of great significance to accurately predict the flight trajectory of flying vehicles using the known historical position data. In this paper, we investigate eight typical types of maneuvers of flying vehicles. Then, according to the kinematics model of the flying vehicle, eight kinds of typical maneuver trajectory equations are introduced. Next, the data set for neural network training is created by varying the critical parameters of the trajectory equation. Accordingly, we train the offline Long-Short Term Memory (LSTM) using 1/12 of the dataset of trajectories, and results show that the trained network can classify types of trajectories accurately. Based on the results of classification using the offline LSTM, we propose two trajectory prediction methods, and both of them achieve excellent prediction with and without noise interference. Furthermore, we propose an online prediction method, and it can accurately predict the subsequent trajectory of flying vehicles when the type of trajectory is not clear. And we propose a filter to improve the accuracy of the online prediction method for trajectory prediction with noise. The simulation results verify that all three previously proposed methods can accurately predict the next move based on historical data. [ABSTRACT FROM AUTHOR]

Minimum lap-time planning (MLTP) is a well-established problem in the race car industry to provide guidelines for drivers and optimize the vehicle's setup. In this paper, we tackle the 3D nature of the problem in its full extension, making no simplifying assumptions on the mechanics of the system. We propose a multibody vehicle model, described by rigorous dynamical equations. To effectively handle the resulting complexity, we devised an efficient direct dynamics computational method based on Featherstone's articulated-body algorithm (ABA). To solve the MLTP, we employed a direct-collocation technique, discretizing the problem so that all information of the 3D track is pre-processed and directly embedded into the discrete problem. This discretization approach turns out to be perfectly compatible with our vehicle model, leading to a solution in accessible computational time frames. The high level of detail of the model makes the proposed approach most useful for in-depth vehicle dynamics analyses on complex tracks. To substantiate the analysis, we provide a comparison with the results obtained by a double-track model on the Nürburgring Nordschleife circuit. Consistently with the average trend defined by the double track, the proposed model features a more dynamically rich behavior, realistically capturing the higher-order effects elicited by the sharp corners and the highly variable slope of the track. [ABSTRACT FROM AUTHOR]

Vehicle stability control strategies can improve driving safety effectively; however, there is still a lack of unified evaluation criteria for different control strategies. This paper proposes a vehicle control strategy evaluation method based on the driving stability region and is analyzed by using direct yaw moment control (DYC) and four-wheel steering (4WS) as examples. Firstly, the five-degree-of-freedom (5DOF) vehicle system models including DYC and 4WS are established, and the effectiveness of the control strategies is verified by nonlinear analysis methods; the dynamic characteristics of the system are also analyzed. Following this, a hybrid algorithm combining the Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) methods is used to solve the system equilibrium points, and the driving stability regions under different control strategies are obtained. Finally, the driving stability regions are tested based on the CarSim and Simulink simulations, and the control performance is evaluated. The results indicate that DYC and 4WS can improve vehicle stability and expand the range of driving stability regions. When the initial longitudinal velocity is below 30 m/s, the driving stability regions under DYC and 4WS expand to different extents compared to the original driving stability region. The expanded driving stability regions show that the stability region of the vehicle with DYC is larger than that of 4WS; thus, the control effect of DYC is better than that of 4WS. The proposed method can be used to evaluate the effective range of different control strategies. [ABSTRACT FROM AUTHOR]

The aerodynamic evaluation of vehicles body structure represents a significant stage in a car body design, having a considerable implication in fuel reduction and an increased vehicle stability on the road. The aim of this paper is to determine the aerodynamic coefficient, illustrating the airflow streamlines around the body of a VAN and reducing the aerodynamic coefficient by adding air deflectors to the vehicle body structure. The starting point of the CFD aerodynamic study is represented by the determination of the air flow regime around the vehicle body, followed by the determination of the aerodynamic coefficient and the lift coefficient for the base model of the vehicle. In the second stage the reduction of the aerodynamic coefficient is done by adding the air deflectors in the rear side of the vehicle. The aerodynamic study is performed for each vehicle model separately, resulting three simulation cases at a 25, 35 and 45 m/s velocity. The last part of the paper presents the conclusions of the obtained results and the strong and weakness points by using the CFD simulation in vehicles aerodynamics evaluation. [ABSTRACT FROM AUTHOR]