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

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

This 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]

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 vehicle logo contains the vehicle's identity information, so vehicle logo detection (VLD) technology has extremely important significance. Although the VLD field has been studied for many years, the detection task is still difficult due to the small size of the vehicle logo and the background interference problem. To solve these problems, this paper proposes a method of VLD based on the YOLO-T model and the correlation of the vehicle space structure. Aiming at the small size of the vehicle logo, we propose a vehicle logo detection network called YOLO-T. It integrates multiple receptive fields and establishes a multi-scale detection structure suitable for VLD tasks. In addition, we design an effective pre-training strategy to improve the detection accuracy of YOLO-T. Aiming at the background interference, we use the position correlation between the vehicle lights and the vehicle logo to extract the region of interest of the vehicle logo. This measure not only reduces the search area but also weakens the background interference. We have labeled a new vehicle logo dataset named LOGO-17, which contains 17 different categories of vehicle logos. The experimental results show that our proposed method achieves high detection accuracy and outperforms the existing vehicle logo detection methods. [ABSTRACT FROM AUTHOR]

Accurate estimations and assessments of vehicle emissions can support decision-making processes. Current emission estimation tools involve several calculation methods that provide estimates of the exhaust components that result from driving on urban arterial roads. This is an important consideration, as the emissions generated have a direct impact on the health of pedestrians near the roads. In recent years, there has been an increase in the use of emission models, especially in combination with traffic simulator models. This is because it is very difficult to obtain an actual measurement of road emissions for all vehicles travelling along the analysed road section. This paper concerns a review of selected traffic simulations and the estimation of exhaust gas components models. The models presented have been aggregated into a group with respect to their scale of accuracy as micro, meso, and macro. This paper also presents an overview of selected works that combine both traffic and emission models. The presented literature review also emphasises the proper calibration process of simulation models as the most important factor in obtaining accurate estimates. This work also contains information and recommendations on modelling that may be helpful in selecting appropriate emission estimation tools to support decision-making processes for, e.g., road managers. [ABSTRACT FROM AUTHOR]

Vehicle identification and re-identification is an essential tool for traffic surveillance. However, with cameras at every corner of the street, there is a requirement for private surveillance. Automated surveillance can be achieved through computer vision tasks such as segmentation of the vehicle, classification of the make and model of the vehicle and license plate detection. To achieve a unique representation of every vehicle on the road with just the region of interest extracted, instance segmentation is applied. With the frontal part of the vehicle segmented for privacy, the vehicle make is identified along with the license plate. To achieve this, a dataset is annotated with a polygonal bounding box of its frontal region and license plate localization. State-of-the-art methods, maskRCNN, is utilized to identify the best performing model. Further, data augmentation using multiple techniques is evaluated for better generalization of the dataset. The results showed improved classification as well as a high mAP for the dataset when compared to previous approaches on the same dataset. A classification accuracy of 99.2% was obtained and segmentation was achieved with a high mAP of 99.67%. Data augmentation approaches were employed to balance and generalize the dataset of which the mosaic-tiled approach produced higher accuracy. [ABSTRACT FROM AUTHOR]

This paper deals with the control of a hydraulically interconnected suspension system to improve the handling performance of the vehicle. A seven-degrees-of-freedom MATLAB/Simulink vehicle model and an Adams/Car model are used for simulation purposes. A Simscape model for analysing the hydraulic subsystem is also employed. It was observed that the yaw rate and roll responses of the vehicle are influenced to a large extent by the flow control valve opening values. The handling performance of the vehicle was investigated by the control of the orifice valves and their adjustment according to the lateral acceleration inputs. Considerable improvements were obtained in the handling qualities without sacrificing the ride responses, compared to the responses of a passive interconnected suspension. [ABSTRACT FROM AUTHOR]

Purpose: Metro wheels running on different lines can undergo wear at different positions. This paper aims to investigate the effects of wheel wear at two typical positions, i.e. wheel flange and tread, on the dynamic performance of metro vehicles and analyzes the differences, with an aim of providing theoretical support on wheel reprofiling for different metro lines. Design/methodology/approach: Wheel profile data were measured on two actual metro lines, denoted A and B. It was observed that wheel wear on Lines A and B was concentrated on flanges and treads, respectively. A metro vehicle dynamics model was built using multibody dynamics software SIMPACK. Then it was applied to analyze the differences in effects of wheel wear at different positions on vehicle dynamic performance (VDP) for various speeds (50, 60 and 70 km/h) and line conditions (straight line, R1000m, R600m and R300m curves). Critical speed and vibration acceleration were used as indicators of VDP during linear motion (on straight track), while VDP during curvilinear motion (on curved track) was evaluated in terms of wheel/rail lateral force, wheel/rail vertical force, derailment coefficient and wheel unloading rate. Findings: First, compared to wheel profile with tread wear, wheel profile with flange wear showed better performance during linear motion. When the distance traveled reached 8 × 104 and 14 × 104 km, the vehicle's critical speed was 12.2 and 21.6% higher, respectively. The corresponding vertical and lateral vibration accelerations were 59.7 and 74.8% lower. Second, compared to wheel profile with flange wear, that with tread wear showed better performance during curvilinear motion, with smaller wheel/rail lateral force, derailment coefficient and wheel unloading rate. When the vehicle speed was 50, 60 and 70 km/h, the maximum difference in the three indicators between the two wheel profiles was 40.2, 44.7 and 23.1%, respectively. For R1000m, R600m and R300m curves, the corresponding maximum difference was 45.7, 69.0 and 44.4%, respectively. Practical implications: The results of the study can provide a guidance and theoretical support on wheel reprofiling for different metro lines. On lines with large proportions of curved sections, metro vehicles are more prone to wheel flange wear and have poorer dynamic performance during curvilinear motion. Therefore, more attention should be paid to flange lubrication and maintenance for such lines. On lines with higher proportions of straight sections, metro vehicles are more prone to tread wear and have poorer performance on straight sections. So, tread maintenance and service requires more attention for such lines. Originality/value: Existing research has focused primarily on the effects of wheel wear on VDP, but fails to consider the differences in the effects of wheel wear at different positions on VDP. In actual metro operation, the position of wheel wear can vary significantly between lines. Based on measured positions of wheel wear, this paper examines the differences in the effects of wheel wear at two typical positions, i.e. tread and flange, on VDP in detail. [ABSTRACT FROM AUTHOR]

For vehicle state estimation, the conventional Kalman filter performs well under the Gaussian assumption, but in the real non-Gaussian situation (especially when the noise is non-Gaussian heavy-tailed), it shows poor accuracy and robustness. In this paper, an extended Kalman filter (EKF) algorithm based on the maximum correntropy criterion (MCC) is proposed (MCCEKF), and a lateral-longitudinal coupled vehicle model is established, while a state observer containing the yaw rate, vehicle sideslip angle, and longitudinal vehicle speed is designed using the easily available measurement information of on-board sensors. After analyzing the complexity of the proposed algorithm, the new algorithm is verified on the Simulink/CarSim simulation experimental platform by Double Lane Change and Sine Sweep Steer Torque Input maneuver. Experimental results show that the MCC-based EKF algorithm has stronger robustness and better estimation accuracy than the traditional EKF algorithm in the case of non-Gaussian noise, and the MCCEKF is more applicable for vehicle state estimation in practical situations. [ABSTRACT FROM AUTHOR]

Next-generation accurate vehicle localization and connectivity technologies will enable significant improvements in vehicle dynamics control. This study proposes a novel control function, referred to as pre-emptive braking, which imposes a braking action if the current vehicle speed is deemed safety-critical with respect to the curvature of the expected path ahead. Differently from the implementations in the literature, the pre-emptive braking input is designed to: a) enhance the safety of the transient vehicle response without compromising the capability of reaching the cornering limit, which is a significant limitation of the algorithms proposed so far; and b) allow — in its most advanced implementation — to precisely constrain the sideslip angle to set levels only through the pre-emptive control of the longitudinal vehicle dynamics, without the application of any direct yaw moment, typical of conventional stability control systems. To this purpose, a real-time-capable nonlinear model predictive control (NMPC) formulation based on a double track vehicle prediction model is presented, and implemented in its implicit form, which is applicable to both human-driven and automated vehicles, and acts as an additional safety function to compensate for human or virtual driver errors in extreme conditions. Its performance is compared with that of: i) two simpler — yet innovative with respect to the state-of-the-art — pre-emptive braking controllers, namely an NMPC implementation based on a dynamic point mass vehicle model, and a pre-emptive rule-based controller; and ii) a benchmarking non-pre-emptive rule-based trail braking controller. The benefits of pre-emptive braking are evaluated through vehicle dynamics simulations with an experimentally validated vehicle model, as well as a proof-of-concept implementation on an automated electric vehicle prototype. [ABSTRACT FROM AUTHOR]

Because the traditional computing model can no longer meet the particularity of Internet of Vehicles tasks, aiming at its characteristics of high bandwidth, low latency and high reliability, this paper proposes a resource allocation strategy for Internet of Vehicles using reinforcement learning in edge cloud computing environment. First, a multi-layer resource allocation model for Internet of Vehicles is proposed, which uses the cooperation mode of edge cloud computing servers and roadside units to dynamically coordinate edge computing and content caching. Then, based on the construction of communication model, calculation model and cache model, make full use of idle resources in Internet of Vehicles to minimize network delay under the condition of limited energy consumption. Finally, the optimization goal is solved by two-layer deep Q network model, and the best resource allocation plan is obtained. The simulation results based on the Internet of Vehicles model show that the computational energy consumption and system delay of proposed strategy do not exceed 400 J and 600 ms, respectively. Besides, the overall effect of resource allocation is better than other comparison strategies and it has certain application prospects. [ABSTRACT FROM AUTHOR]

In control theory, a controller parameter-tuning method called virtual reference feedback tuning (VRFT) is proposed. VRFT can tune a feedback controller using single experimental data. If VRFT can be applied to a vehicle, the yaw-rate controller can be adequately tuned without the need for a vehicle model. Therefore, this study expands the results of VRFT to realize the desired yaw-rate control of autonomous vehicles. The key point of the proposed method is to realize the auto-tuning of the gain-schedule proportional-integral (PI) controller based on the vehicle velocity. In this study, the validity of the proposed method using a vehicle dynamics block set is verified. [ABSTRACT FROM AUTHOR]

Vehicle re-identification (ReID) is to find a particular vehicle across different surveillant cameras in city roads. There are two main challenges in vehicle ReID. In one case, pictures of the same vehicle from different camera views often look different, which is called "intra-instance difference" in this paper. In another case, different vehicles of the same model and color often look similar from the same camera view, which is called "inter-instance similarity" in this paper. To deal with these challenges, we design a super-resolution-based part collaboration network (SPCN) for vehicle ReID, which aims to improve the performance of ReID system by paying attention to local features with strong differentiation. First, a fine-grained object detection method is used to find the discriminative local parts in vehicle images, and the image qualities of these local parts are enhanced by a super-resolution model with long skip connections. Then, a part collaboration mechanism is designed to realize the weighted fusion of local features, since different local features show different importance in the process of vehicle ReID task. Finally, the global features are fused with the local features to produce the identification results. Extensive experiments on two public data sets show the effectiveness of our method, which introduces less background noise and achieves state-of-the-art results. [ABSTRACT FROM AUTHOR]

Accurate acquisition of vehicle dynamics state information is essential for vehicle active safety control systems. However, these states cannot be easily measured, and the measurement is expensive. Conventional Kalman filters perform well for vehicle state estimation in Gaussian environments but exhibit low accuracy and robustness under practical non-Gaussian noise. Vehicle model parameter ingestion, inaccurate tire force calculation, and non-Gaussian noise from on-board sensors cause great challenges to the estimation of vehicle driving states. Therefore, this paper presents a robust hierarchical estimation scheme for vehicle driving state based on the maximum correntropy square-root cubature Kalman filter (MCSCKF) using easily measurable on-board sensor information. First, the vehicle mass is dynamically updated based on the recursive least squares (FRLS) method with a forgetting factor. Then, an adaptive sliding mode observer (ASMO) is designed to estimate the longitudinal and lateral tire forces. Ultimately, the vehicle states are estimated based on the MCSCKF under non-Gaussian noise. Two typical operating situations are carried out to verify the validity of the proposed estimation scheme. The results prove that the proposed estimation scheme can estimate the vehicle's driving state accurately compared to other common methods. And the MCSCKF algorithm has better accuracy and robustness than the traditional Kalman filters for vehicle state estimation in non-Gaussian situations. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the measured values of key state parameters such as the lateral velocity and yaw rate of the vehicle are easily interfered by random errors, a filter estimation method of vehicle state is proposed based on the principle of robust filtering and the unscented particle filter algorithm. Based on the establishment of a 3-DOF non-linear dynamic model and the Dugoff tire model of the vehicle, the adaptive robust unscented particle filter(ARUPF) is used to filter and estimate the parameters of the vehicle state, and to realize the longitudinal and lateral speed as well as the yaw rate of the vehicle during the driving process. The simulation and the real vehicle test results show that based on the adaptive robust unscented particle filter algorithm, the vehicle driving state estimation can be realized, the measurement parameters can be effectively filtered, and the estimation accuracy is high. [ABSTRACT FROM AUTHOR]

We investigate the dynamics of a two‐wheel vehicle model after deviating from an unstable steady powerslide motion. For a model with brush tyre characteristics we observed that for a range of constant driver inputs (front steering angle, driving torque) the motion converges to a periodic oscillation around a circular path with a very small radius of curvature. In this paper we investigate, how a modified tyre model with decreasing friction coefficient for large slip velocities influences the stability and domain of existence for these periodic motions. We also compare the obtained solutions with those using Pacejka's Magic Formula for the tyre characteristics and find a remarkable good agreement. [ABSTRACT FROM AUTHOR]

To investigate and compare the lane changing behavior of passenger cars and heavy vehicles during the implementation period (defined as the interval from the start time to the end time of a lane change maneuver), this study applies the gradient boosting decision tree (GBDT) method to model the lane changing behavior of heavy vehicles and passenger cars, respectively. Results show that the lane change models vary with the vehicle types and lane change directions. Different factors are considered by the drivers of passenger cars and heavy vehicles when implementing lane changes to different directions. Partial dependence plots of GBDT models reveal that the influence of independent variables on lane changing behavior is nonlinear and complicated, which means that the same variable leads to various effects on the lane change decision across different vehicle types and lane change directions. In contrast with other state-of-the-art methods, the proposed method can obtain more accurate results. The findings indicate that it is necessary to build specific lane change models based on vehicle types and lane change directions for microscopic traffic simulators and autonomous vehicles. [ABSTRACT FROM AUTHOR]

The inerter is a two-terminal component that can be added to the spring-and-damper configuration of a suspension system. It has the property that the force exerted is proportional to the relative acceleration at its terminals. Studies have demonstrated the inerter's benefit of providing superior vibration isolation when it is used in the vehicle suspension of passenger cars. However, similar benefit on another common vehicle class on the roads, namely heavy vehicles, remain to be shown, as these vehicles have vastly different parameter values than passenger cars. This study is an investigation on the performance improvement brought by an inerter in the suspension of common heavy vehicles. In the study, the parameter values of a truck and a bus were adopted in the quarter vehicle model with two different spring-damper-inerter configurations (parallel and serial inerter), and the improvements in vibration isolation and road holding capability were determined by optimization of inertance. Results show that the inerter is similarly effective in providing the said improvements when implemented on heavy vehicles instead of on passenger cars, judging from reductions in sprung mass acceleration and dynamic tire load. It is also observed that the performance benefit is associated with larger optimum inertance than that for passenger cars. Overall, the inerter has been shown to be beneficial in the parallel and serial configurations, both of which are common and can be practically implemented in the suspension of heavy vehicles. [ABSTRACT FROM AUTHOR]

This study proposes an approach to the yaw and lateral stability of the vehicle that considers their steady-state trend. This study designs a new linear time-varying model predictive control (LTV-MPC) method based on the 2DOF vehicle model. The tire model curve of the magic formula is simplified into two straight lines to replace the nonlinear characteristic of tires and reduce the solution time of the controller under the premise of a good tracking effect. The lateral stiffness of tires is selected according to the predicted trend of stable state of the vehicle. Simulation results show that, under extreme conditions, the proposed method can effectively track the reference vehicle sideslip angle and yaw rate. The real-time performance of the controller is substantially improved compared with NMPC. [ABSTRACT FROM AUTHOR]

This paper proposes the dissipation of the energy analysis approach for vehicle plane motion stability analysis. The vehicle uses a three-degree-of-freedom vehicle model, and a unified tire-sideslip-angle model suitable for large tire-sideslip-angle and wheel reverse rotation conditions. Two calculation methods of total dissipation of energy are proposed: the integral calculation method and the energy difference calculation method. This paper analyses the system stability of the vehicle model, using the total dissipation of the energy indicator and compares the results with the Lyapunov exponent indicator. It shows that the dissipation of the energy analysis approach has a fast calculation speed, strong interpretability and high calculation accuracy, which can intuitively and quantitatively reveal the global stability characteristics of vehicle plane motion. [ABSTRACT FROM AUTHOR]

The high-speed steering control of tracked vehicles has been studied thoroughly, but the high-speed steering stability boundary of tracked vehicles has rarely been investigated. The establishment of a dynamics model with a high computational accuracy is the premise of studying the high-speed steering stability boundary of tracked vehicles. In this work, the track forces of tracked vehicles during steering were determined based on a shear stress model, and the steering dynamics model of tracked vehicles was established. A zero-differential-steering tracked vehicle was used as the object of study. The calculation results of the steering kinematics model and dynamics model of tracked vehicles under different steering conditions were studied in detail on sandy road surface environment, as well as the change laws of the steering trajectory, steering radius, vehicle centroid velocity, and steering slip angle. The steering trajectories of tracked vehicles on five kinds of roads under the same steering conditions were determined by simulations. By setting the critical condition for unstable steering, the corresponding relationship between the maximum circumferential velocity difference of the sprocket Δ u max , the minimum steering radius R min , the maximum sideslip angle β max , and the theoretical centroid speed v t h was obtained when the vehicle turned on five kinds of roads, and it was used to determine the high-speed steering stability boundary of tracked vehicles. The simulation results can provide a reference for the design of tracked vehicle steering mechanisms and high-speed steering control. [ABSTRACT FROM AUTHOR]

Aimed at the safety and stability problems of intelligent vehicles under extreme conditions such as low adhesion road surface and emergency lane change and obstacle avoidance, this article designs a lane change and obstacle avoidance controller based on road adhesion coefficient. Using the nonlinear vehicle dynamics model as the prediction model, using the recursive least squares method to identify the road adhesion coefficient, considering the road adhesion coefficient to plan and adjust in the obstacle avoidance path as well as limit constraint conditions of the model predictive control controller, using model predictive control method for the expectation of intelligent vehicle trajectory tracking, travels tremendously guarantee the security and stability of driving. The joint CarSim–Simulink simulations results show that under poor road conditions, the trajectory tracking accuracy after optimization is higher and the vehicle is less prone to sideslip and instability. The lane change controller designed in this article has strong adaptability to different road surface adhesion coefficient, and all parameters can be controlled within a reasonable safety range at different speeds, with good robustness. [ABSTRACT FROM AUTHOR]

This paper investigates the attitude control problem of the morphing vehicle subject to great dynamics changes and disturbances during the morphing phase. An improved model‐free adaptive control (MFAC) method is proposed based on the compact format dynamic linearization technique. Firstly, a compact format data model and data‐driven control scheme are established based on the input/output (I/O) data of the controlled plant, independent of the complicated and time‐varying mathematical model of the morphing vehicle. Secondly, a series of historical output data errors in a moving time window are introduced to the control law, in which the length of the moving time window can be adjusted according to the system order of controlled plant. The convergence and stability of the improved control law are then proved theoretically, which guarantees the convergence of the tracking errors and the boundedness of the input and output data. Finally, numerical simulations are presented to evaluate the proposed approach, and comparisons are made with the conventional proportional‐derivative control method. Simulation results demonstrate that the improved method possesses better effectiveness and robustness in the presence of model changes and disturbances. [ABSTRACT FROM AUTHOR]

Vehicle Re-Identification (ReID) is of great significance for public security and intelligent transportation. Large and comprehensive datasets are crucial for the development of vehicle ReID in model training and evaluation. However, existing datasets in this field have limitations in many aspects, including the constrained capture conditions, limited variation of vehicle appearances, and small scale of training and test set, etc. Hence, a new, large, and challenging benchmark for vehicle ReID is urgently needed. In this paper, we propose a large vehicle ReID dataset, called VERI-Wild 2.0, containing 825,042 images. It is captured using a city-scale surveillance camera system, consisting of 274 cameras covering a very large area over 200 $km^2$ k m 2 . Specifically, the samples in our dataset present very rich appearance diversities thanks to the long time span collecting settings, unconstrained capturing viewpoints, various illumination conditions, diversified background environments, and different weather conditions. Furthermore, to facilitate more practical benchmarking, we define a challenging and large test set containing about 400K vehicle images that do not have any camera overlap with the training set. VERI-Wild 2.0 is expected to be able to facilitate the design, adaptation, development, and evaluation of different types of learning models for vehicle ReID. Besides, we also design a new method for vehicle ReID. We observe that orientation is a crucial factor for feature matching in vehicle ReID. To match vehicle pairs captured from similar orientations, the learned features are expected to capture specific detailed differential information for discriminating the visually similar yet different vehicles. In contrast, features are desired to capture the orientation invariant common information when matching samples captured from different orientations. Thus a novel disentangled feature learning network (DFNet) is proposed. It explicitly considers the orientation information for vehicle ReID, and concurrently learns the orientation specific and orientation common features that thus can be adaptively exploited via an adaptive matching scheme when dealing with matching pairs from similar or different orientations. The comprehensive experimental results show the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Directional performance and highway stability are two important aspects that need to be considered in development and design of a heavy articulated vehicles. To improve the maneuverability and stability of a multi-trailer articulated heavy vehicle (MTAHV), an active linear quadratic regulator (LQR) multi-axle-steering method is designed and examined. First, a linear yaw-plane model with four-degree-of freedom (4-DOF) for MTAHV is built and validated. Thus, a reference model supplying the desired state responses is introduced. Then, an active control algorithm of multi-axle-steering for the rear axles of tractor and full-trailer is investigated, and a LQR controller is proposed based on the linear vehicle model to make the control variables track the desired state responses. The control strategy concentrates on keeping the actual yaw rate and side-slip angle follow the steady-state yaw rate and zero side-slip angle. Finally, the effectiveness of the designed approach on enhancing the maneuverability and stability of the MTAHV have been validated through the simulations of the low-speed 360° roundabout and the single lane-change maneuver with high speed, respectively. The method has a certain reference value for improving the active safety of the MTAHV. [ABSTRACT FROM AUTHOR]

With the rise of unmanned driving and intelligent transportation research, great progress has been made in vehicle detection technology. The purpose of this paper is employing the method of deep learning to study the vehicle detection algorithm, in which primary-stage target detection algorithms, namely, YOLOv3 algorithm and SSD algorithm, are adopted. Therefore, the method first processes the image data in the open-source road vehicle dataset for training. Then, the vehicle detection model is trained by using YOLOv3 and SSD algorithms to show the detection effect, respectively. The result is by comparing the detection effects of the two models on vehicles. The researchers accomplished the result analysis and summarized the characteristics of various models obtained by training, to apply to target tracking, semantic segmentation, and unmanned driving. [ABSTRACT FROM AUTHOR]

Under bad weather, the ability of intelligent vehicles to perceive the environment accurately is an important research content in many practical applications such as smart cities and unmanned driving. In order to improve vehicle environment perception technology in real hazy scenes, we propose an effective detection algorithm based on Swin Transformer for hazy vehicle detection. This algorithm includes two aspects. First of all, for the aspect of the difficulty in extracting haze features with poor visibility, a dehazing network is designed to obtain high-quality haze-free output through encoding and decoding methods using Swin Transformer blocks. In addition, for the aspect of the difficulty of vehicle detection in hazy images, a new end-to-end vehicle detection model in hazy days is constructed by fusing the dehazing module and the Swin Transformer detection module. In the training stage, the self-made dataset Haze-Car is used, and the haze detection model parameters are initialized by using the dehazing model and Swin-T through transfer learning. Finally, the final haze detection model is obtained by fine tuning. Through the joint learning of dehazing and object detection and comparative experiments on the self-made real hazy image dataset, it can be seen that the detection performance of the model in real-world scenes is improved by 12.5%. [ABSTRACT FROM AUTHOR]

This article deals with the control chassis dynamics of a light electric vehicle. In the frame of Global Chassis Control, it proposes a hierarchical control approach focuses on compensating the chassis dynamics against driver disturbances. It is based on a supervisory structure consists of two main levels, a global controller for regulating chassis variables and a local controller to each suspension system. Both controllers are designed using a fractional-order robust controller, namely CRONE controller, to provide an optimized solution for the performance-robustness tradeoff. The main objective is to improve ride comfort for passengers while respecting road holding and handling criteria. In contrast to other studies, random road disturbances are considered to investigate the limits of the proposed approach. Analysis in frequency and time domain has been done to evaluate the performance and robustness of the designed controller. Simulation results based on a full nonlinear 14 degree of freedom vehicle model show that the proposed strategy can effectively improve the ride quality in the field of interest where a significant performance in driver disturbance rejection is reported. [ABSTRACT FROM AUTHOR]

Centrifugal force is what causes the vehicle to roll over. This force is generated when the vehicle suddenly changes direction when moving at high speed. The solution of using a stabilizer bar is suggested to minimize this phenomenon. There are currently three types of stabilizer bars in use: mechanical stabilizer bars, hydraulic stabilizer bars, and electronic stabilizer bars. The content of this article is aimed at introducing and reviewing the characteristics of the stabilizer bar. Besides, two oscillation simulation models of vehicles equipped with stabilizer bars are also analysed in this article. Additionally, the characteristics of the control algorithms for the stabilizer bar are clearly analysed. The half-dynamics model is suitable for algorithms that need to use oscillatory state matrices such as SMC, LQR, and LQG. The spatial dynamics model is suitable for some algorithms such as PID, fuzzy, and neural. The roll angle of the vehicle has been significantly improved when the stabilizer bar is fitted. In general, the stability and safety of the vehicle can be guaranteed if the vehicle uses a stabilizer bar. [ABSTRACT FROM AUTHOR]

In the peak period of double-layer line, in the process of calculating the optimal solution of vehicle planning, assigning an optimal solution to each constraint will greatly waste computing resources. In the process of vehicle logistics route planning, the natural characteristics of unbalanced constraints will affect the flow stability of vehicles in the planning process. This paper studied the influence of unbalanced constraints on the flow stability of vehicle bilevel route planning . It constructed a two-level programming Stack elberg model for vehicle logistics lines . The upper part was mainly used to restrict the capacity and structure of vehicle logistics lines and eliminate unavaila ble routes. The lower part mainly implemented balanced distribution of vehicle logistics lines to prevent too many vehicles from crowding in the same route. It used model to integrate scheduling business into high capacity routing . It added the scheduling resource cache technology to the model to ensure the stability of the scheduling process. The experimental results show that, compared with the single model solution, using the model to schedule the business grooming can achieve a more stable performance to a large extent, and solve the flow stability problem of vehicle logistics line planning . [ABSTRACT FROM AUTHOR]

Future vehicle localisation technologies enable major enhancements of vehicle dynamics control. This study proposes a novel vehicle stability control paradigm, based on pre-emptive control that considers the curvature profile of the expected path ahead in the computation of the reference direct yaw moment and braking control action. The additional information allows pre-emptive trail braking control, which slows down the vehicle if the predicted speed profile based on the current torque demand is deemed incompatible with the reference trajectory ahead. Nonlinear model predictive control is used to implement the approach, in which also the steering angle and reference yaw rate provided to the internal model are varied along the prediction horizon, to account for the expected vehicle path. Two pre-emptive stability control configurations with different levels of complexity are proposed and compared with the passive vehicle, and two state-of-the-art nonlinear model predictive stability controllers, one with and one without non-pre-emptive trail braking control. The performance is assessed along obstacle avoidance tests, simulated with a high-fidelity model of an electric vehicle with in-wheel motors. Results show that the pre-emptive controllers achieve higher maximum entry speeds – up to ∼34% and ∼60% in high and low tyre-road friction conditions – than the formulations without preview. [ABSTRACT FROM AUTHOR]

This study deals with drifting as one of the most important nonlinearities in vehicle dynamics. A four-wheel vehicle model is used to calculate the equilibria in planar motion, numerically, by introducing an assumption on constant longitudinal tyre slips. Other than the unstable drifting points that were reported by previous researchers, a new pair of drifting equilibria are identified and the difference between the two types is studied. The phase portrait approach is used to identify the type of these equilibria, and they reveal the type of instability in the primary and the secondary drifting points, and provide control suggestions to stabilise the drifting equilibria. It is observed that the rear-wheel-drive vehicles have primary drifting equilibria, while the four-wheel-drive vehicles have secondary drifting equilibria. There is a transition point between the primary and the secondary drifting types when the front longitudinal tyre slips increase from zero to saturation. It is further shown that primary drifting becomes stable by regulating the yaw rate at the equilibrium value; whereas the secondary drifting may be stabilised through a constant forward velocity. The stabilisation methods are applied through control designs and validated in simulations. [ABSTRACT FROM AUTHOR]

Longitudinal velocity, lateral velocity, and front wheel steering angle are crucial states for vehicle active safety features. However, direct measurement of these variables requires expensive measurement instruments and can be easily affected by vehicle nonlinear dynamics, outliers and noise pollution. Moreover, unknown inputs bring great challenges to their accurate estimation. Therefore, this paper develops a novel robust unscented M-estimation-based filter (RUMF) for vehicle state estimation with unknown driver steering torque. The nonlinear system model is constructed based on the vehicle dynamics model. Unscented transformation (UT) and statistical linearization are implemented to transform the nonlinear process and measurement function into a linear-like regression form with data redundancy to achieve outlier suppression. Then, an M-estimation-based iterated algorithm is designed to address process uncertainty and innovation and observation outliers for robust vehicle state estimation. The iteratively reweighted least-squares (IRLS) method based on the M-estimation methodology is utilized for unknown input estimation. Simulations and experimental results compared with extended Kalman filter (EKF) and particle filter (PF) have verified the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The phenomenon of vehicle rollover usually occurs when the driver suddenly steers at high speed. The centrifugal force will appear and cause the vehicle's body to tilt. To overcome this situation, the solution of using the stabilizer bar is proposed. The passive stabilizer bar has a simple structure, low cost, and long service life. As a result, it is now a standard on most vehicles. This paper has established a dynamic model to describe a vehicle's oscillation. As a result of the study, the maximum roll angle of the vehicle was reduced from 9.0° to 8.2° when the stabilizer bar was used. Besides, the minimum value of the vertical force at the wheel reached 485(N) and 1162(N), respectively, corresponding to the two survey cases. The movement trajectory of the vehicle when using the stabilizer bar does not have a big difference compared to the case when the vehicle does not use the stabilizer bar. So, the vehicle's stability and safety can be effectively improved. This is the basis for further developing complex stable bar patterns in the future. [ABSTRACT FROM AUTHOR]

This article formulates a front-wheel-drive three degree-of-freedom (3DOF) four-wheel planar vehicle model with the Magic Formula tire model. The state variables' evolutions of the model, i.e., trajectories of the model under acceleration and deacceleration conditions, are analyzed. The process of evolution is divided into desirable and undesirable phases based on the response characteristics of the vehicle to the driver input during the process. The trajectories are categorized as unsaturated trajectories and saturated trajectories by the existence of saturated tires during these phases. The response of state variables to driver input under acceleration conditions during undesirable phases are zero or even opposite, while the response of undesirable phases under the deacceleration condition is partially positive. Besides, the existing yaw rate safety envelope is recalibrated by using a longitudinal and lateral tire force coupling model. A more accurate yaw rate safety envelope is obtained from the given driver input. Furthermore, a longitudinal speed safety envelope is proposed according to the relationships among slip angle, yaw rate, and longitudinal speed. These safety envelopes are determined by driver input, tire properties, and grip condition. After overlaying yaw rate and longitudinal speed safety envelopes in the state space, the feasibility of using the safety envelope as trajectory classification criteria is discussed. [ABSTRACT FROM AUTHOR]

A method to solve minimum-lap-time problems using quasi-steady-state models and free (i.e. not predetermined) trajectory on two-dimensional tracks has been recently proposed. This work describes the extension of the method to three-dimensional tracks and builds upon g–g–g diagrams (instead of the standard g–g), to account for the effects of three-dimensionality. The main features of car and motorcycle g–g–g diagrams are discussed, to get insight into the main effects of three-dimensionality and to suggest a convenient parametrisation for the subsequent optimal control problem (OCP), whose size is not affected by the complexity of the vehicle model employed to generate the g–g–g diagrams. The application of the method to the Mugello and Barcelona–Catalunya circuits is given, with vehicle datasets resembling those of a race motorcycle (MotoGP) and a race car (Formula One). The results obtained are in line with those reported in the literature using full-dynamic models, yet such dynamic models are generally associated with the solution of much larger OCP. [ABSTRACT FROM AUTHOR]

Aiming at solving problem of vehicle state estimation, an adaptive fading unscented Kalman filter(AFUKF) algorithm was proposed. Based on this purpose, a 7-DOF nonlinear vehicle model with the Pacejka nonlinear tire model was established firstly. Then, the vehicle state estimator based on Kalman filter was designed to solve the problem of vehicle state estimation. The simulation verification shows the effectiveness and reliability of the designed estimator for vehicle state estimation. Compared with other traditional methods, the calculation accuracy is higher for the AFUKF algorithm to solve the problem of vehicle state estimation. The study can help drivers easily identify key state estimation in safe driving area. [ABSTRACT FROM AUTHOR]

Motivated by military applications, this work considers connected platoons of ground vehicles of potentially different sizes and presents a model-free approach for optimizing the speed of the platoon to adjust the tradeoff between fuel economy and mobility as measured by travel speed. The motivation to seek a model-free solution is twofold: (1) vehicle models that are typically assumed to be available in model-based methods are not available on-board for military vehicles; (2) a model-free solution can offer robustness to modeling errors. Therefore, in this paper, the problem of optimizing the tradeoff between fuel economy and mobility of a mixed platoon is formulated as an optimization problem and solved using the model-free Nelder-Mead approach. To explore the performance characteristics of this approach, a case study is performed with two different size vehicles that are representative of military trucks, both in simulation and in a novel networked engine-in-the-loop setup. The results show that the proposed approach can achieve the desired balance between fuel economy and mobility in a model-free manner despite the nonlinearity caused by gear shift deadzones, albeit at the expense of relatively slow convergence time. In addition, a design guideline for the parameters in the Nelder-Mead approach is also discussed. [ABSTRACT FROM AUTHOR]

Simulation computations represent a very effective tool for investigating operational characteristics and behaviours of vehicles without having a real product. The rail vehicles sector is typical, in that simulation computations including multibody modelling of individual vehicles (i.e., wagons) as well as entire trainsets are widely used. In the case of designing rail vehicles, running safety and ride comfort are two of the most important assessment areas. The presented work is focused on the research of the dynamical effects of a rail vehicle while running on a railway track created in a commercial multibody model. There is a lot of research focused on the investigation of dynamic performances while a rail vehicle is running on a flexible railway track. The real operation of a rail vehicle meets problems on track, where the stiffness-damping parameters of a railway track vary in transient sections (e.g., the exit of a tunnel). This work brings a contribution to research related to the assessment of the dynamic response of a rail vehicle on a chosen track section. A passenger railway vehicle is chosen as a reference multibody model. Simulation computations were performed for three different railway track models, i.e., for a rigid track model and for a flexible track model defined in two different manners. The stiffness-damping parameters of the rail vehicle are defined symmetrically in relation to the longitudinal axis of the vehicle, e.g., they are the same values for the left and right side. The centre of gravity is not located symmetrically, but it is partially shifted in the lateral direction. This can be observed in the results of wheel forces and their waveforms. There are evaluated values and waveforms of the vertical wheel forces, the lateral wheel forces and the derailment quotient. The obtained results have revealed the influence of the railway track formulation in the model on the output parameters. [ABSTRACT FROM AUTHOR]

A hierarchical vehicle collision avoidance control method based on model predictive control is presented in this study. In the upper level of the controller, a spatial-based two-degree-of-freedom vehicle model is used for dynamic path planning to decrease the computational burden of the algorithm. Obstacles and road boundaries are translated into spatial-based constraints on system states. An objective function that considers tire adhesion margin is introduced to the path tracking controller to enhance vehicle safety. Meanwhile, the dynamic constraints of vehicle lateral acceleration, sideslip, and tire slip angles are designed in accordance with the tire-road adhesion coefficient. A time-based nonlinear model predictive controller is also designed and compared with the proposed method to verify the effectiveness and superiority of the latter. Theoretical analyses and simulation results indicate that the proposed collision avoidance control system has good collision avoidance effect. [ABSTRACT FROM AUTHOR]

This paper presents a novel estimation and identification approach for lateral vehicle dynamics. The algorithm leverages on a Linear Fraction Transform (LFT) reformulation of vehicle and tyre models, allowing for a simple and computationally efficient inclusion of complex and nonlinear dynamic models, like, for example, two-wheels, four-wheels or single-track as vehicle model, and Pacejka, brush or Fiala as tyre model. As a result, this technique can be easily adopted in the development of an online identification system, able to run on a standard embedded device, implementing a flexible identification procedure that can handle different driving conditions, up to the limits of handling, different vehicle modelling approaches, and different input measurements. Experimental results demonstrate the effectiveness of the proposal, either in a persistent excitation and in a non-persistent excitation scenario. [ABSTRACT FROM AUTHOR]

This paper investigates the nonlinear dynamical behaviors of a time-delayed supercavitating vehicle system varying with the system parameters, and proposes control strategies to stabilize the trajectory. A simplified dynamical model of the supercavitating vehicle is firstly constructed and then analyzed on its local and global stability in terms of different parameter values and initial conditions. Due to the forces resulting from the fins and the afterbody plane causing one of the system parameters to become a piecewise constant function of position, the system is described as a linear switched one with time delay. Both theoretical analysis and numerical simulations are adopted to describe the system behaviors. Several special phenomena such as bifurcations, deviation behaviors and period-doubling processes emerging from the system for a given set of parameters are discussed in detail in this paper. Besides, the equilibrium and chaotic solutions are accurately located by plotting the Lyapunov exponent map. Chaotic attractors present different strange forms with the variation of the parameters. Finally, based on the system's global property, the control and anti-control strategies are proposed to stabilize the system or generate desirable chaos and it is shown that the system can be controlled by selecting appropriate control parameters. [ABSTRACT FROM AUTHOR]

The deep-sea mining miner requires good passability to operate in complex and changeable terrain environment of the seabed. Among them, the track tension is an important factor to ensure the normal running of the vehicle. Aiming at the complex track tension problem on track link, the forces on each component of the tracked system were analyzed, and the theoretical formulas of track tension were established. The theoretical estimation and dynamic simulation of track tension in uniform speed were carried out by using the multibody dynamics model of the tracked vehicle, and the rationality of the theoretical estimation formula was verified. The influencing factors of track tension also were analyzed, and the significance of each factor on track was discussed by dynamic simulation. The results provided a theoretical basis for design of tracked vehicles. [ABSTRACT FROM AUTHOR]

Path planning is a critical part for improving the driving safety and driver comfort of autonomous vehicles (AVs), especially in complex maneuvering conditions. In addition, different drivers have different preferences for AVs, thus, how to provide personalized trajectories for different drivers is a vital issue for AVs. The collision-free path planning problem in conditions with large road curvatures is investigated in this paper, with the consideration of environmental safety constraints, drivers’ comfort, vehicle actuator constraints, etc. Firstly, a Driver-Vehicle-Road (DVR) system is established based on the combination of the kinematic vehicle model and the two-point visual preview driver model, such that the driver's individual handling characteristics can be considered in the controller. The kinematic vehicle model is modified to have the similar understeering characteristics with those of the nonlinear full car models, and then the proposed DVR system can satisfy different groups of drivers and cars. Secondly, for environmental constraints, a new artificial potential field (APF) method is proposed, which can form a banana-shaped 3-D dangerous imaginary mountain and a lane boundary cliff suitable for arbitrary curvature roads to generate a collision-free evasive path. Finally, the Linear-Time-Varying (LTV) model predictive control (MPC) method is adopted to design the path planner. The CarSim-Simulink joint simulation illustrates that with the proposed planner, the host vehicle is capable of avoiding obstacles with a safer and more comfortable maneuver on large curvature roads. And the proposed path planner can provide individually safe trajectories for different drivers with good maneuverability. [ABSTRACT FROM AUTHOR]

In order to improve steering and roll stability for a heavy vehicle, a novel MIMO (multi-input-multi-output) controller is proposed based on an AMFC theory. A vehicle model considering the lateral motion, yaw motion and roll motion is developed. The establishment of reference models, controlled models and iteration algorithm of the feed-back matrix are presented. The Newton iteration method is used to correct the feed-back matrix in the real time, and the ideal control signal is calculated by the integral feed-forward matrix. The state of controlled model can approach reference model to achieve an adaptive online update for the steering control parameters under the transient conditions. The proposed AMFC have a good improvement in reducing side-slip angle, yaw rate and roll angle. The simulation and HIL experiments demonstrate the effectiveness and reliability of the proposed controller in improving the yaw stability and thus preventing rollover when necessary. [ABSTRACT FROM AUTHOR]

In this article, the connectivity‐maintained platoon control problem for networked vehicles with external disturbance is considered. By modeling each vehicle as a nonlinear triple integral system, a specified‐time sliding model disturbance observer and a specified‐time controller are presented to ensure the convergence of the platoon system. The predetermined performance method is applied to maintain the connectivity of vehicles in the platoon, which enables flexibility in the communication connectivity between vehicles. The specified time sliding mode disturbance observer is proposed to estimate the external disturbance within the predefined time and it is proved to be uniformly ultimate boundedness stable by using the time transformation method. A dynamic gain controller with the time constraint is proposed to achieve the stable platoon with connectivity maintained for each vehicle. The proposed approach can simultaneously guarantee the specified‐time convergence and connectivity maintenance without depending on any system parameters. The effective of the proposed controller is proved by the Lyapunov function. Finally, comparative simulation results are obtained to verify the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

In this paper, we consider the specified-time platoon control for the networked vehicles modelled by the third-order dynamics with uncertain external bounded disturbance. The vehicles are subjected to disturbance and parameter uncertainties. To ensure the platoon can converge within a predefined time, a novel disturbance observer with a time-varying dynamic gain is proposed. By the time transformation approach, the disturbance observer is proved to be uniformly ultimate bounded (UUB) stable in a specified time. The time-varying dynamic gain is also applied to design the novel specified-time controller for the vehicle platoon and rigorous proof is presented based on Lyapunov stability. Under the proposed disturbance observer and controller, the vehicle platoon can be stabilised within the predefined time without depending on any system parameters and initial conditions. Finally, some comparison simulation results are given to verify the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]