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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]

The Vehicle Routing Problem (VRP) is a crucial subject in the discipline of logistics and transportation management since it connects the distribution centers to the arrival points of services and goods in the most efficient way possible. Considering the VRP in the framework of operational research, the optimization quality of the VRP mainly depends on an efficient model built for the network and the successful choice for the objective of the model. The optimization problem, which involves the objective function as the ratio of two functions, is described as Fractional Programming (FP), which has attracted noteworthy attention in the previous five decades because of its usefulness in modeling several decision processes in operations research, management science, and economics. In this research, we have studied a Linear Fractional Vehicle Routing Problem (LFVRP) regarding the contribution of the mathematical modeling of VRP to both the optimization literature and the commercial market, which is incontrovertible. For this purpose, we propose an iterative method for LFVRP that aims to optimize the delivery of goods and services while minimizing the rate of total cost/load without any variable transformation technique proposed for the first time. By this methodology of iterative optimization, unlike the literature, the mathematical complexity of the model will be facilitated as a Linear Programming Problem (LPP) and, meanwhile, provide the capability of considering multiple objectives (both cost and load) as one. Furthermore, by this advantage, it has been able to express a green-based approach by presenting an objective that minimizes fuel consumption which constructs transportation expenses, and hence it lowers its carbon footprint for our world while keeping the aim of maximum load. In order to illustrate the effectiveness of our approach, we have built a real-life model with real data in the retail sector in Türkiye and provided a comparative analysis. [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]

The maneuverability of supercavitating vehicles is significantly affected by disturbance from their control surfaces during the water-entry process. In this paper, experiments and simulations on cavity evolution models and vehicle dynamic characteristics are carried out to investigate the influence of the water-entry disturbance on the water-entry maneuvering motion process of supercavitating vehicles. In the water-entry experiment of medium speed projectiles based on the high speed photography, the shapes of cavity contours are extracted and the applicability of the cavity evolution models to the problem of water-entry maneuvering motion is verified. A kind of more correct prediction model of cavity shape is chosen by the comparison. A mathematical model on the water-entry maneuvering motions of vehicles is developed based upon certain simplifications, which makes an improvement on the three-dimensional cavity topology algorithm. Afterward, the simulations on the water-entry maneuvering motion of the vehicle are carried out, from which the real-time interaction between the vehicle and the cavity is extracted and the influence law of the fin deflection angle on the vehicle dynamic characteristics is analyzed. The influence mechanism of the desynchronization of fins' contacting water on the water-entry maneuvering motions of vehicles is revealed. It is found that the fin deflection angle of 10° is the most conducive to enhancing the vehicles' ability to stabilize their attitudes during water-entry maneuver by analyzing the fin efficiency and planning parameters. [ABSTRACT FROM AUTHOR]

Summary: The focus of this paper is an integrated, fault‐tolerant vehicle control algorithm for the overall stability of ground vehicles. The proposed scheme comprises a high‐level controller that creates a virtual control input and a low‐level adaptive control allocator that distributes the virtual control effort among redundant actuators. The proposed control framework distinguishes itself from earlier results in the literature by its ability to blend active suspension, steering and traction control channels, in the presence of uncertainties and time‐varying dynamics, without the need for fault identification. The control structure is validated in the simulation environment using a fourteen‐degree‐of‐freedom non‐linear vehicle model. The integrated controller is compared to the case of a conventional control approach where each control problem is solved separately. Our results show that, compared to the conventional approach, the proposed method ensures that the vehicle follows driver inputs with up to 33$$ \mathbf{33} $$% higher longitudinal maneuver velocity, despite the presence of actuator failures and slippery road conditions. Furthermore, to demonstrate the benefit of integrating active suspension control to the overall control scheme, we replaced the suspension control of the proposed approach with an independent suspension control system for comparison purposes. We then showed that the integrated case provided 24$$ \mathbf{24} $$% lower roll angle deviation, and 20$$ \mathbf{20} $$% lower pitch angle deviation, in the presence of actuator effectiveness loss and adverse road conditions. [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]

To reduce occupant injuries in collisions between vehicles and roadside trees, this paper introduces the acceleration severity index (ASI) as an indicator of occupant injury risks and uses the vehicle dynamics simulation software PC-crash to carry out offset crash tests of vehicles and trees along the roadside. The recommended diameter of roadside trees with the minimum ASI is given by collecting various ASI data corresponding to different vehicle departure speeds and slope gradients, and the multivariate models of vehicle departure speeds, slope gradients and diameters of roadside trees, as well as the minimum ASI, are fitted. In the case of the minimum ASI >1, by further adjustment of the diameter of roadside trees, the calculation method of the distance to trees is proposed, and the optimized design schemes of the roadside tree diameter and spacing with the minimum ASI ≤1 for different vehicle departure speeds and slope gradients are given. The results show that the vehicle departure speed is positively logarithmic to the recommended diameter of roadside trees and is positively exponential to the minimum ASI. The slope gradients are approximately positively linearly correlated with the tree recommended diameter and minimum ASI. When the slope gradients are 1:6.5~1:3.5 with a vehicle departure speed of 100 km/h~120 km/h, when the slope gradients are 1:2.5 with a vehicle departure speed of 90 km/h~120 km/h, and when the slope gradients are 1:1.5 with a vehicle departure speed of 80 km/h~120 km/h, the corresponding minimum ASI >1, which indicates that the severity of roadside accidents exceeds the limit that an occupant can endure. [ABSTRACT FROM AUTHOR]

Vehicle classification is widely used in intelligent transportation and smart cities. However, the good performance of vehicle classification often depends on a large number of labeling data, which leads to the high cost of manual labeling. How to use a small number of labeled samples with rich features to achieve good classification performance is a challenge in vehicle classification. To solve these problems, we propose a deep active learning framework called Feature Fusion Spatial Pyramid Pooling and Re -parameterization Visual Geometry Group (FFSPP-RepVGG). The framework is mainly divided into two parts: query strategy and feature extraction module. Specifically, the query strategy uses Feature Fusion (FF) to calculate the loss, thus defining uncertainty and being able to select more valuable samples for labeling training. The feature extraction module uses Spatial Pyramid Pooling and Re -parameterization Visual Geometry Group (SPP-RepVGG) model, which can extract more reliable image features. It can reduce the cost of data labeling and have better performance at the same time. The experimental results on the BIT-Vehicle data set and the car-10 data set show that the FFSPP-RepVGG framework has superior performance than that of the comparison models. [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]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Systems engineering has become important in almost every complex product manufacturing industry, especially automotive. Emerging trends like vehicle electrification and autonomous driving now pose a System of Systems (SoS) engineering challenge to automotive OEMs. This paper presents a proof‐of‐concept (PoC) that applies a top‐down SoS perspective to Hyundai‐Kia Motor Corporation's (HKMC) virtual product development process to develop a performance‐critical component of the vehicle, the tire. The PoC demonstrates using the Arcadia MBSE method to develop a consistent, layered, vehicle architecture model starting from the SoS operational context down to the lowest level of system decomposition in the physical architecture thereby capturing top‐down knowledge traceability. Using the concept of functional chains, several vehicle performance views are captured that serve as the basis for architecture verification orchestration across engineering domains using a cross‐domain orchestration platform thereby validating key vehicle/tire performance metrics that influence the tire design parameters. Preliminary results of the study show that applying a method‐based modeling approach could provide several benefits to HKMC's current product development approach such as reduced time to model, SoS knowledge capture and reusability, parameter/requirement traceability, early performance verification, and effective systems engineering collaboration between the OEM, tire design supplier and tire manufacturers. [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]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

The past few years of the COVID‐19 pandemic outbreak have shown that optimal management of medical specimens is a key aspect of healthcare logistics, both for addressing the delivery of perishable items such as vaccines, and for ensuring the timely analysis of swabs and samples. Accordingly, recent optimization literature described the problem of optimal collection and delivery of medical specimens, modeled as a multitrip vehicle routing problem with time windows and a completion time objective function. Aiming to achieve good‐quality solutions in short computational times, this work describes a hybrid approach, combining a matheuristic construction phase with an adaptive large neighborhood search (ALNS). Our matheuristic relies on a clustering algorithm to yield subsets of the medical specimens that are optimally served by single vehicles of the fleet. The solutions of the matheuristic phase serve as starting points for the ALNS intensification phase. Extensive experimentation on both new and established benchmark problem instances shows that our hybrid method is able to match the optimality of the state of the art on small instances and outperforms the existing exact method by one order of magnitude on larger problems. [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]

Under the impaction of excitation forces of the elastic tire/rigid drum and off-road soil ground interactions, the vibratory roller's ride quality had been strongly influenced. Thus, the vibration isolation models and structures of the vehicle's cabin and seat are studied and improved for enhancing the vehicle's ride quality. To have an overview of the study and development process of the seat suspensions and cabin isolations, this research provides the overall view of the seat and cabin's vibration isolation models developed and applied to the vibratory rollers based on the results of the existing studies. The study shows that with the semiactive hydraulic isolations (HIs) or semiactive pneumatic-hydraulic isolations (PHIs) applied to the cabin's vibration isolations, the cabin shaking and driver's ride quality have been remarkably ameliorated, while, with quasi-zero stiffness elements (QSE) added into the seat suspension, the driver's ride quality has been greatly ameliorated. Based on the studied results, to control the cabin shaking and enhance the driver's ride quality, a combination between the cabin's semiactive HIs and the seat's QSE should be developed in further studies. [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 introduction of more automation into our vehicles is increasing our ability to avoid or mitigate the effects of collisions. Early systems could brake when a likely collision was detected, while more advanced systems will be able to steer to avoid or reconfigure a collision during the same circumstances. This paper addresses how the post-impact motion of an impacted vehicle could be included in the decision-making process of severity minimisation motion planning. A framework is proposed that builds on previous work by the authors, combining models from motion planning, vehicle dynamics, and accident reconstruction. This framework can be configured for different contexts by adjusting its cost function according to relevant risks. Simulations of the unified system are carried out and analysed from the perspective of vehicle model complexity and collision parameters sensitivity. Additionally, effects are highlighted concerning different modelling decisions, with respect to vehicle dynamics models and collision models, that are important to consider in further research. [ABSTRACT FROM AUTHOR]