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Electric vehicles are considered as an emerging solution to mitigate the environmental footprint of transportation sector. Therefore, researchers and automotive developers devote significant efforts to enhance the performance of electric vehicles to promote broader adoption of such technology. One of the critical challenges of the electric vehicle is limited battery lifetime and entailed range anxiety. In his context, development of counter-aging control strategies based on precise battery modeling is regarded as an emerging approach that has a significant potential to address battery degradation challenges. This paper presents a combined trade-off strategy to minimize battery degradation while maintaining acceptable driving performance and charge retention in electric vehicles. A battery aging model has been developed and integrated into a full vehicle model. An optimal control problem has been formulated to tackle the afore-mentioned challenges. Non-dominant sorting genetic algorithms have been implemented to yield the optimal solution through the Pareto-front of three contending objectives, based upon which an online simulation has been conducted considering three standard driving cycles. The results reveal the ability of the proposed strategy to prolong the life cycle of the battery and extend the driving range by 25 % and 8 % respectively with minimal influence of 0.6 % on the driveability. [ABSTRACT FROM AUTHOR]

The enhanced model reference adaptive control (EMRAC) algorithm is an effective full‐state adaptive solution to control plants affected by nonlinear unmodelled dynamics and persistent disturbances. The EMRAC strategy improves the tracking performance by equipping the MRAC algorithm with adaptive switching and adaptive integral control actions. However, the need for the plant state prevents the applicability of the EMRAC algorithm to engineering control problems where only the plant output is measurable. To cover this gap and extend the range of plants controllable by EMRAC solutions, this article presents an output‐based EMRAC algorithm leveraging the closed‐loop (CL) MRAC formulation. The robustness of the closed‐loop control system is analytically analysed, not only with respect to plant parameter uncertainties and square measurable disturbances, but also to ℒ∞$$ {\mathcal{L}}_{\infty } $$ unmodelled terms and disturbances. The ultimate boundedness of the closed‐loop control system is assessed with respect to ℒ∞$$ {\mathcal{L}}_{\infty } $$ unknown nonlinear terms and disturbances, by using Lyapunov theory for Filippov systems, as the adaptive switching control action makes the closed‐loop system discontinuous. To assess the effectiveness of the CL‐EMRAC strategy to impose reference trajectories despite the unmodelled plant dynamics and persistent bounded disturbances, the problem of vehicles' direct yaw moment control is used as an engineering case study. The closed‐loop tracking performance is also quantitatively evaluated through a set of key performance indicators and compared to those provided by four benchmark controllers, that is, two LQ‐based strategies and two MRAC‐based control solutions. The CL‐EMRAC and benchmark controllers are implemented and tested in a co‐simulation environment based on a high‐fidelity IPG CarMaker vehicle model. [ABSTRACT FROM AUTHOR]

The precision of path tracking in high‐speed intelligent vehicles is significantly influenced by model mismatch arising from factors like parameter uncertainty, model simplification, external disturbances, and other sources. In this paper, we propose a novel robust control strategy that integrates the compensation function observer (CFO) with the model predictive control (MPC) method, utilizing an optimized vehicle dynamics model (opt‐model) to address this challenge, called OCMPC. Initially, we establish the opt‐model to design predictive model by leveraging suspension kinematics and compliance (K&C) data collected from a miniature pure electric vehicle. Remarkably, the opt‐model exhibits improved accuracy compared to the conventional vehicle dynamics model (con‐model) while preserving the same degrees of freedom (DOF). Next, we incorporate CFO into the path tracking process of high‐speed intelligent vehicles, enabling dynamic real‐time observation of the model mismatch between the prediction model and the actual vehicle. CFO can capture the dynamics of the vehicle, including nonlinearities and uncertainties, without placing a heavy computing burden on the controller. This observed mismatch is subsequently employed for feed‐forward compensation, facilitating the attainment of optimal control values. Ultimately, we validate the effectiveness of our proposed method in enhancing path tracking accuracy for high‐speed intelligent vehicles through co‐simulation using Simulink and Carsim. [ABSTRACT FROM AUTHOR]

ABSTRACT This article considers the trajectory tracking problem of automated guided vehicles with unknown‐but‐bounded noises and proposes a set‐membership filtering (SMF) based model predictive control strategy. A 2‐DOF vehicle model is first established with considering the unknown‐but‐bounded process and measurement noises. Then, a trajectory tracking control scheme is proposed which consists of two parts, that is, a set‐membership observer and a model predictive controller. The proposed set‐membership observer is able to deal with the unknown‐but‐bounded noises and obtain the state estimation ellipsoid for the considered automated guided vehicle. Further, the designed model predictive controller utilizes the estimated ellipsoid to compute the control sequence. Finally, simulation results demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The coupled, dynamic loads from vehicles travelling on a bridge can cause excessive vibration of the bridge and limit its service life. The coupled vehicle–bridge interaction (VBI) is strongly determined by the properties of the vehicle suspension, the vehicle speed, and the bridge flexibility. A bridge-friendly vehicle is designed to minimize the contact force between the tires and the bridge, enhancing the vehicle’s performance and protecting the bridge from damage by optimizing the vehicle suspension system. In this paper, we investigate the concept of bridge-friendly vehicle by optimally tuning the parameters of the vehicle suspension to mitigate the bridge vibration. The vehicle is modeled with an inerter–spring–damper (ISD) suspension. A time-frozen technique is applied to obtain the time-varying displacement transmissibility functions of the coupled system, and effects of the suspension inerter on the time–frequency characteristics of the transmissibility are discussed. The fixed-point principle is applied to derive the optimal values of the ISD, which are shown to depend only on a priori known structural parameters of the bridge and vehicle parameters. Numerical simulations of the coupled system responses are presented to demonstrate the effectiveness of the tuned ISD in mitigating the vibration of the vehicle and the bridge. Effects of considering the inerter in the suspension model in this tuning problem for bridge-friendliness are also discussed. [ABSTRACT FROM AUTHOR]

New energy vehicles (NEVs) are considered a crucial means of reducing travel costs, enhancing consumer experiences, and innovating services. This paper aims to categorize the functional experiences of NEVs into two types: intelligent experience and eco-friendly experience, using a dual-path model. By analyzing 118,648 text data from automotive information and service platforms, the relevant factors influencing consumer satisfaction are explored. The research findings reveal that intelligent experience has a significantly positive impact on consumer satisfaction, whereas eco-friendly experience has a significantly negative impact on consumer satisfaction. This suggests that new energy vehicle companies need technological innovation in infrastructure and range to enhance consumer satisfaction. Furthermore, the research confirms that, when facing the intelligent experience of new energy vehicles, consumers' "rational" thinking plays a dominant role, and efficacy is an intermediate variable to enhance consumer satisfaction. On the other hand, when facing the ecofriendly experience of new energy vehicles, consumers' "emotional" thinking predominates, and identity is an intermediate variable to enhance consumer satisfaction. Additionally, the moderating effect of perceived usability is examined. When faced with the intelligent experience of NEVs, consumers with high perceived usability are more likely to generate a sense of efficiency. In the case of the eco-friendly experience of NEVs, consumers with low perceived usability are more likely to experience a sense of identity. [ABSTRACT FROM AUTHOR]

The temperature of axle box bearing is always one of the indicators used to monitor whether the high-speed train is running safely. In this paper, a thermal model of axle box bearing considering the change of bearing lubrication state and the operating condition of the high-speed train was established. Based on the analysis of the generation mechanism of frictional heat inside the bearing, the influence of proportion of grease involved in lubrication was introduced for the first time and a detailed temperature transfer system of the axle box system was established. At the same time, through the axle box bearing-vehicle system dynamics model, the vertical and lateral loads of the axle box bearing under different operating condition were obtained as input parameters of the axle box bearing thermal model, the temperature distribution curve of the bearing under different operating conditions was obtained. Finally, based on the long-term wireless transmission device system (WTDS) temperature monitoring data, the calculation results of the established model were verified. The verification results showed the reliability of the model established in this paper, and showed the necessity of considering the change of the internal lubrication state of the bearing when evaluating the axle box bearing temperature. [ABSTRACT FROM AUTHOR]

The advent of autonomous vehicles has brought about significant advancements in transportation technology, promising safer and more efficient means of travel. However, their full integration into society depends on the accuracy of path-tracking realised by a lateral controller. Lateral control is achieved by regulating the steering angle to minimise the lateral error between the vehicle and a target point at a look-ahead distance on the reference path. This paper investigates the look-ahead distance as it is considered a key parameter that impacts vehicle performance, stability, and energy consumption. A qualitative analysis is performed to deduct a set of rules to adapt the look-ahead distance to three parameters: vehicle velocity, road curvature, and road adherence. Then, an original explicit mathematical formulation is developed for the look-ahead distance as a function of the considered parameters. A fuzzy logic decision for the look-ahead distance is further established and compared with the formulated one. Both approaches are implemented on a look-ahead distance-based lateral controller based on the super-twisting sliding mode control. Simulation results carried out in a joint simulation between Simulink/MatLab and SCANeR $ ^{{\rm TM}} $ TM Studio vehicle dynamics simulator demonstrate the effectiveness of the developed model on vehicle performance, stability, computational efficiency, and energy consumption. [ABSTRACT FROM AUTHOR]

Recent advances in mobile mapping systems have greatly enhanced the efficiency and convenience of acquiring urban 3D data. These systems utilize LiDAR sensors mounted on vehicles to capture vast cityscapes. However, a significant challenge arises due to occlusions caused by roadside parked vehicles, leading to the loss of scene information, particularly on the roads, sidewalks, curbs, and the lower sections of buildings. In this study, we present a novel approach that leverages deep neural networks to learn a model capable of filling gaps in urban scenes that are obscured by vehicle occlusion. We have developed an innovative technique where we place virtual vehicle models along road boundaries in the gap-free scene and utilize a ray-casting algorithm to create a new scene with occluded gaps. This allows us to generate diverse and realistic urban point cloud scenes with and without vehicle occlusion, surpassing the limitations of real-world training data collection and annotation. Furthermore, we introduce the Scene Gap Completion Network (SGC-Net), an end-to-end model that can generate well-defined shape boundaries and smooth surfaces within occluded gaps. The experiment results reveal that 97.66% of the filled points fall within a range of 5 centimeters relative to the high-density ground truth point cloud scene. These findings underscore the efficacy of our proposed model in gap completion and reconstructing urban scenes affected by vehicle occlusions. [ABSTRACT FROM AUTHOR]

In this study, an extended Interval-Valued Picture q-rung Orthopair Fuzzy Set (IVPq-ROFS) is developed, which indicates a trio-different combination of multi-granulated fuzzy numbers, and their basic operation and score function are discussed. An IVPq-ROFS was utilized for sustainability-focused transportation-sectorized electric vehicle battery charging technology selection problem among seven attributes and three alternatives. The application parameters are prioritized through innovative Integrated Objective and Subjective Weighted Approach (IOSWA) for attribute importance identification and Measurement of Alternatives and Ranking according to Compromise solution (MARCOS), which comes under Multi-Attribute Decision Making (MADM) model. This research paper elucidates efficiency of the proposed MADM approach under IVPq-ROFS environment and suggesting alternative battery swapping is best option for electric vehicle charging technology for more adaptation in society for future green mobility. The proposed computational decision algorithm results are validated through nine comparative and sensitivity cases, which proves the reliability and higher effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

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Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office 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.)

In the model of the vehicle recognition algorithm implemented by the convolutional neural network, the model needs to compute and store a lot of parameters. Too many parameters occupy a lot of computational resources making it difficult to run on computers with poor performance. Therefore, obtaining more efficient feature information of target image or video with better accuracy on computers with limited arithmetic power becomes the main goal of this research. In this paper, a lightweight densely connected, and deeply separable convolutional network (DCDSNet) algorithm is proposed to achieve this goal. Visual Geometry Group (VGG) model is improved by utilizing the convolution instead of the fully connected module, the deeply separable convolution module, and the densely connected network module, with the first two modules reducing the parameters and the third module allowing the algorithm to have more features in a limited number of parameters. The algorithm achieves better results in the mine vehicle recognition dataset. Experiments show that the recognition accuracy is improved by 4.41% compared to VGG19 and the amount of parameters is reduced by 71% compared to VGG19. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive procedure for the parametric design and analysis of a underwater vehicle rotary docking skirt (RDS) structure. Different types of software were integrated to improve the design efficiency, and different techniques were used to establish an approximate model of the RDS structure with high fitting accuracy. Nondominated sorting genetic algorithm II (NSGA-II) was improved by dynamic adjustment of the population size according to the number of iterations and new crossover and mutation operations (i.e. INSGA-II), which was then applied to a multi-objective optimisation model of the RDS structure. A Pareto solution set was obtained, and a sensitivity analysis was performed on the design variables and objective functions of four representative optimisation schemes. These results can be used to provide a richer selection for designers to reference when designing RDS structures. Abbreviations: RDS; Rotary docking skirt; NSGA-II; Nondominated sorting genetic algorithm II [ABSTRACT FROM AUTHOR]

In this study, the Bald Eagle Search Algorithm performed hydrogen consumption and battery cycle optimization of a fuel cell electric vehicle. To save time and cost, the digital vehicle model created in Matlab/Simulink and validated with real-world driving data is the main platform of the optimization study. The digital vehicle model was run with the minimum and maximum battery charge states determined by the Bald Eagle Search Algorithm, and hydrogen consumption and battery cycle values were obtained. By using the algorithm and digital vehicle model together, hydrogen consumption was minimized and range was increased. It was aimed to extend the life of the parts by considering the battery cycle. At the same time, the number of battery packs was included in the optimization and its effect on consumption was investigated. According to the study results, the total hydrogen consumption of the fuel cell electric vehicle decreased by 57.8% in the hybrid driving condition, 23.3% with two battery packs, and 36.27% with three battery packs in the constant speed driving condition. [ABSTRACT FROM AUTHOR]

In complex terrain, such as uneven roads or irregular terrain, two-wheeled heavy-duty self-balancing vehicles are easily affected by external interference factors, causing rollover or rendering the vehicle unable to move, which poses a greater challenge to its stability control. Therefore, it is necessary to establish a kinematic model of a two-wheeled vehicle and design a control system to study its driving stability. This paper aims to study the stability control system of a two-wheeled self-balancing vehicle under complex terrain. First, a self-balancing vehicle modeling method based on complex terrain is designed. By analyzing the motion characteristics of the self-balancing vehicle, a kinematic model suitable for complex terrain is established, which provides a basis for subsequent control algorithms. Secondly, a precise control system is designed for different terrain conditions, and parameters such as vehicle attitude, speed and acceleration are adjusted through the Proportional–Integral–Derivative (PID) control algorithm to achieve the smooth operation of the self-balancing vehicle in complex terrain. In addition, a vehicle-mounted camera is used to capture terrain images in real time, and different terrains are accurately identified through the terrain recognition algorithm based on deep learning, thereby determining the friction coefficient and effectively improving the stability of the self-balancing vehicle on complex terrain. The experimental results show that the designed control system can enable the self-balancing two-wheeled vehicle to achieve stable balance control in different terrains, and has good applicability and stability. [ABSTRACT FROM AUTHOR]

Improvements to ArduSub for the BlueROV2 (BROV2) Heavy, necessary for accurate simulation and autonomous controller design, were implemented and validated in this work. The simulation model was made more accurate with new data obtained from real-world testing and values from the literature. The manual control algorithm in the BROV2 firmware was replaced with one compatible with automatic control. In a Robot Operating System (ROS), a proportional–derivative (PD) controller to assist augmented reality (AR) pilots in controlling angular degrees of freedom (DOF) of the vehicle was implemented. Open-loop testing determined the yaw hydrodynamic model of the vehicle. A general mathematical method to determine PD gains as a function of the desired closed-loop performance was outlined. Testing was carried out in the updated simulation environment. Step response testing found that a modified derivative gain was necessary. Comparable real-world results were obtained using settings determined in the simulation environment. Frequency response testing of the modified yaw control law discovered that the bandwidth of the nonlinear system had a one-to-one correspondence with the desired closed-loop natural frequency of a simplified linear approximation. The control law was generalized for angular DOF and linear DOF were operated with open-loop control. A full six-DOF simulated dive demonstrated excellent tracking. [ABSTRACT FROM AUTHOR]

Scheduling decisions for concurrent emergency response (CER) across multiple disaster sites presents numerous difficulties. The main challenge is to minimize human casualties while taking into account the rationality of resource allocation across different disaster sites. This paper establishes a joint scheduling model for emergency vehicles on freeways in the context of CER. The model aims to minimize the transportation time, dispatch cost, and casualty risk, by using the resource site scheduling scheme as the decision variable, addressing multiple disaster and resource sites. Specifically, a casualty risk function based on the rescue waiting time was designed to balance the competing needs among various disaster sites, enhance equitable resource allocation, and reduce the probability of casualties. To achieve global convergence in a high-dimensional solution space, a tailored heuristic algorithm called adaptive dual evolutionary particle swarm optimization (ADEPSO) is proposed. The numerical results show that the scheduling scheme proposed by the ADEPSO algorithm satisfies all constraints and demonstrates significant advantages in large-sized instances. Compared to the two basic algorithms, ADEPSO provides a more cost-effective scheme and reduces the average rescue waiting time. Moreover, integrating the casualty risk function significantly decreases the average rescue waiting time at both high- and low-priority disaster sites, thereby directly lowering the casualty risk. [ABSTRACT FROM AUTHOR]

This paper focuses on the cooperative driving challenges of connected and automated vehicles (CAVs) at single-lane roundabouts. First, a geometric path planning method is proposed for CAVs navigating a single-lane roundabout. Based on this method, a vehicle roundabout model is established. Four potential traffic scenarios for CAVs are established, and the optimal arrival times at conflict points are analyzed. By correlating the optimal arrival times at conflict points with the optimal entry times into the roundabout, the multi-vehicle coordination problem in complex intersections is simplified to a speed control issue during entry. Utilizing the principles of optimal control and Pontryagin minimization, two speed optimization strategies are proposed. Finally, MATLAB is employed for simulation analysis. The results indicate that the control strategy proposed in this paper enables the system to clearly identify potential conflicts between vehicles and implement an optimal control strategy, ensuring that vehicles can navigate the roundabout efficiently in terms of time and fuel without collisions. Additionally, the minimum time interval is established at 0.2 seconds to completely prevent vehicle collisions. In this study, the fusion problem involving two vehicles at a single conflict point is further expanded to encompass multiple vehicles at multiple conflict points. Thus, the efficient scheduling of multiple vehicles in single-lane roundabouts is realized. [ABSTRACT FROM AUTHOR]

The traffic environment in tunnel entrance area is complex, with prominent issues related to safety and efficiency. However, existing models find it difficult to accurately capture the traffic situation in this area. Therefore, this paper establishes a vehicle dynamics model for the entrance area of a tunnel based on the social force model and artificial potential field. This model can characterize the deceleration behavior of vehicles at the entrance of the tunnel and the interaction between vehicles. By considering the special effects of the tunnel environment, the simulation experiments successfully reproduce the congestion phenomenon in the entrance of the tunnel. Based on this model, this paper proposes a coordinated driving model under vehicle to vehicle (V2V) communication environment, and the simulation results show that this model can effectively alleviate the congestion in the entrance area of the tunnel and improve traffic efficiency. [ABSTRACT FROM AUTHOR]

In response to the issues of low merging success rates and poor safety in the on-ramp merging scenario within autonomous driving, we propose an on-ramp merging model for unmanned vehicles based on the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. Firstly, we introduce an Action-Mask (AM) to prevent the sampling of invalid actions during merging, thus enhancing safety by ensuring only valid actions are considered. Secondly, we incorporate noise advantage values to encourage unmanned vehicles to thoroughly explore the environment and avoid being trapped in local optimal solutions. Experimental results demonstrate that the AM-MAPPO algorithm model improves both safety and traffic efficiency. [ABSTRACT FROM AUTHOR]

In response to the fact that autonomous vehicles cannot avoid obstacles by emergency braking alone, this paper proposes an active collision avoidance method for autonomous vehicles based on model predictive control (MPC). The method includes trajectory tracking, adaptive cruise control (ACC), and active obstacle avoidance under high vehicle speed. Firstly, an MPC-based trajectory tracking controller is designed based on the vehicle dynamics model. Then, the MPC was combined with ACC to design the control strategies for vehicle braking to avoid collisions. Additionally, active steering for collision avoidance was developed based on the safety distance model. Finally, considering the distance between the vehicle and the obstacle and the relative speed, an obstacle avoidance function is constructed. A path planning controller based on nonlinear model predictive control (NMPC) is designed. In addition, the alternating direction multiplier method (ADMM) is used to accelerate the solution process and further ensure the safety of the obstacle avoidance process. The proposed algorithm is tested on the Simulink and CarSim co-simulation platform in both static and dynamic obstacle scenarios. Results show that the method effectively achieves collision avoidance through braking. It also demonstrates good stability and robustness in steering to avoid collisions at high speeds. The experiments confirm that the vehicle can return to the desired path after avoiding obstacles, verifying the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a method for estimating GPS coordinates from visual information captured by a monocular camera mounted on a fixed-wing tactical Unmanned Aerial Vehicle at high altitudes (up to 3000 m) in GPS-denied zones. The main challenge in visual odometry using aerial images is the computation of the scale due to irregularities in the elevation of the terrain. That is, it is not possible to accurately convert from pixels in the image to meters in space, and the error accumulates. The contribution of this work is a reduction in the accumulated error by comparing the images from the camera with satellite images without requiring the dynamic model of the vehicle. The algorithm has been tested in real-world flight experiments at altitudes above 1000 m and in missions over 17 km. It has been proven that the algorithm prevents an increase in the accumulated error. [ABSTRACT FROM AUTHOR]

In vertical vehicle dynamics control, semi-active dampers are used to enhance ride comfort and road-holding with only minor additional energy expenses. However, a complex control problem arises from the combined effects of (1) the constrained semi-active damper characteristic, (2) the opposing control objectives of improving ride comfort and road-holding, and (3) the additionally coupled vertical dynamic system. This work presents the application of Reinforcement Learning to the vertical dynamics control problem of a real street vehicle to address these issues. We discuss the entire Reinforcement Learning-based controller design process, which started with deriving a sufficiently accurate training model representing the vehicle behavior. The obtained model was then used to train a Reinforcement Learning agent, which offered improved vehicle ride qualities. After that, we verified the trained agent in a full-vehicle simulation setup before the agent was deployed in the real vehicle. Quantitative and qualitative real-world tests highlight the increased performance of the trained agent in comparison to a benchmark controller. Tests on a real-world four-post test rig showed that the trained RL-based controller was able to outperform an offline-optimized benchmark controller on road-like excitations, improving the comfort criterion by about 2.5% and the road-holding criterion by about 2.0% on average. [ABSTRACT FROM AUTHOR]

This paper considers external disturbances imposed on vehicle systems. Based on a vehicle dynamics model of the vehicle with three degrees of freedom (3-DOFs), a union disturbance observer (UDO) composed of a nonlinear disturbance observer (NDO) and an extended state observer (ESO) was designed to obtain external disturbances and unmodeled items. Meanwhile, an improved adaptive unscented Kalman filter (iAUKF) with anti-disturbance and anti-noise properties is proposed, based on the UDO and the unscented Kalman filter (UKF) method, to evaluate the sideslip angle of vehicle systems. Finally, a vehicle yaw stability controller was designed based on UDO and the global fast terminal sliding mode control (GFTSMC) method. The results of co-simulation demonstrated that the proposed UDO was effectively able to observe external disturbances and unmodeled items. The proposed iAUKF, which considers external disturbances, not only achieves adaptive updating and adjustment of filtering parameters under different sensor noise intensities but can also resist external disturbances, improving the estimation accuracy and robustness of the UKF. In the anti-disturbance performance test, the maximum estimation error of the sideslip angle of the iAUKF under the three working conditions was less than 0.1°, 0.02°, and 0.5°, respectively. Based on the UDO and the GFTSMC, a vehicle yaw stability controller is described, which improves the accuracy of control and the robustness of the vehicle's stability control system and greatly strengthens the driving safety of the vehicle. [ABSTRACT FROM AUTHOR]

Due to the constrained performance of embedded chips in devices such as drones, real-time processing of simultaneous vehicle detection and road segmentation networks becomes challenging, leading to a lack of associative feature learning. To tackle these issues, we introduce a novel multi-task model for vehicle detection and road segmentation in unmanned aerial vehicle(UAV) Aerial Imagery. Our approach introduces a lightweight Ghost-Dilated convolution, combining the large receptive field of dilated convolution with the efficiency of Ghost convolution, resulting in fewer parameters and reduced computational load. Building upon this, we propose the Ghost-Atrous Spatial Pyramid Pooling (G-ASPP) module, a multi-scale feature extraction module that enhances the model's multi-scale characteristics while minimizing the increase in network parameters and computational requirements associated with Atrous Spatial Pyramid Pooling(ASPP) modules. The constructed multi-task UAV aerial vehicle detection and road segmentation network incorporates a carefully designed backbone, neck, detection head, and segmentation head. By refining existing lightweight backbone networks, our model achieves superior real-time performance and accuracy, demonstrating enhanced detection and segmentation accuracy with lower parameters and computational overhead. Experimental validation on a self-constructed multi-task dataset highlights the proposed model's improved segmentation and detection performance, particularly for small targets and narrow roads, confirming its effectiveness. This research contributes valuable insights to the study of multi-task networks in the realm of UAV vision. [ABSTRACT FROM AUTHOR]

Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers. [ABSTRACT FROM AUTHOR]

The electrification of commercial vehicles has led to new wheel torque allocation options for propulsion and braking of articulated heavy vehicles. Each unit or axle can be individually braked or propelled whilst the other units or axles are excluded from such action. This may achieve the best energy efficiency. However, this can also lead to potential yaw stability problems such as jackknifing and trailer swing, especially under bad loading and weather conditions. This paper describes the above instabilities and introduces a nonlinear single-track model to study the vehicle dynamics of the tractor-semitrailer combination. The effects of different vehicle and environment parameters are analysed with this vehicle model. A safe operating envelope for limiting the wheel forces is obtained using this vehicle model. Since the vehicle model introduced in this paper is computationally effective, it can be run online in real vehicles, with an instantaneous safe operating envelope obtained for the momentary conditions. Thus, yaw instabilities such as jackknifing and trailer swing may be avoided. [ABSTRACT FROM AUTHOR]

To address the issues of curvature discontinuity and terminal tire non-return in the parallel parking of autonomous vehicles, a novel parallel parking path planning method based on the combination of the quintic polynomial curve and an improved sigmoid function was proposed. First, a vehicle kinematic model was established. Second, considering the position, front wheel angle, and yaw angle constraints during the parking process, a hybrid superimposed curve was designed. The parking path planning problem was converted into an optimal control problem, with the maximum curvature and curvature at both ends as objective functions, and the parameters were optimized using the simulated annealing algorithm. Subsequently, for path tracking control, the simulated annealing algorithm was used to optimize the prediction time horizon of the model predictive control algorithm. Finally, a series of actual vehicle experiments were conducted based on the Apollo Autonomous Driving Developer Suite, and the effectiveness of the proposed path planning method was validated. Therefore, this method can provide a certain reference for automatic parking path planning technology. [ABSTRACT FROM AUTHOR]

To solve the complicated steering control of small vehicles in the agriculture and difficult steering on complex roads, this study designed a wheel-track composite vehicle. The vehicle incorporated a novel power differential steering mechanism with dual driving, enabling steering through the differential rotation of the rear two wheels. The vehicle is simple to control, small in size, and is able to work under the conditions of complex roads, such as hills, mountains, and muddy land. The study initially focused on presenting the design, theoretical analysis, and dynamic simulation analysis of the power differential steering mechanism with dual driving. Subsequently, the vehicle underwent modeling and simulation using UG software to validate the reasonability of the values. Finally, utilizing test data, four mathematical models for the actual steering radius of the vehicle on four road surfaces were derived through neural network fitting. The maximum relative error between the model results and the actual steering radius value was reported to be 3.53%. The advantages of the vehicle included continuous radius steering, deceleration and torsion increase, differential lock, etc. This made it well-suited for applications in all-terrain military and civilian vehicles, as well as various special equipment mobile platforms equipped with walking devices. [ABSTRACT FROM AUTHOR]

In this paper, the removal effects and activation energy of Ce and Pd doping on pollutants (CO, C3H6, and NO) were comparatively analyzed by using characterization methods and constructed kinetic equations. Furthermore, the problems of the water influence mechanism on the NSR process were also discussed. The results show the following: (1) Pd doping effectively improves the removal of CO (80%) and C3H6 (71%) in the low-temperature section of the catalyst (150–250 °C) compared to Ce doping, while Ce doping exhibits excellent low-temperature conversion of NO. (2) The reaction activation energy of the LaKMnPdO3 catalyst was 9784 kJ/mol, which was significantly lower than that of the LaKMnCeO3 catalyst. (3) The presence of H2O has an important enhancement effect in the storage performance of the LaKMnPdO3 catalyst for NOx but decreases the catalytic reduction of NO. It provides a solution for the effective treatment of the increasing problems of particulate matter and ozone pollution. [ABSTRACT FROM AUTHOR]

Unexpected tire blowout on expressway is one of the most dangerous scenarios for heavy load multi-axle trucks, especially under the inexperienced driver's improper intervention. To this end, this paper proposes an autonomous tire blowout controller for three-axle trucks based on model predictive control (MPC) method. First of all, aiming at balancing the computing load and the path-tracking accuracy, an equivalent chassis is transformed based on the stable two-dimensional vehicle dynamic model. Second, a tire blowout–oriented vehicle high-fidelity dynamic model is established based on the equivalent chassis, in which the vehicle lateral, yaw, and roll motions are all considered at the same time. Then, an MPC-based dynamic control strategy is proposed to achieve effective tire blowout suppression with the consideration of vehicle rollover stability. Afterward, a transient vertical tire force shock is employed to simulate the real tire blowout behavior, on which two typical scenarios are constructed based on the TruckSim–Simulink co-simulation platform. The simulation results illustrate the feasibility and effectiveness of the proposed method. In two different dangerous tire blowout conditions, the lateral load transfer rate (LTR) value of the vehicle is reduced by 3.45% and 40.87%, respectively, while the trajectory tracking error is reduced at the same time. [ABSTRACT FROM AUTHOR]

This paper proposes an event-triggered model predictive-preview control strategy for autonomous vehicle trajectory tracking. First, the dynamic equation is established based on the preview road curvature and the vehicle's 2 degree-of-freedom relationship. A model predictive tracking controller is designed by predicting the system dynamics in the future. Second, to reduce the computational burden of the controller, the triggering conditions are designed according to the system's stability and feasibility. The proposed event-triggered model predictive-preview control strategy not only makes the autonomous vehicles maintain tracking accuracy but also reduces the number of online optimization. Then, asymptotically stable of closed-loop systems are proved by using Lyapunov's theorem. Finally, simulation experiments show that the proposed strategy has some advantages over traditional model predictive control in terms of improving vehicle tracking accuracy and reducing algorithm complexity. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive investigation into the impact of active aerodynamic load balancing (AALB) on the cornering performance of high-performance vehicles. The research explores the use of active asymmetric aerodynamic devices, specifically split and tilted rear wing concepts, capable of manipulating vertical wheel loads and counteracting effects of lateral load transfer during cornering. The performance potential of AALB is assessed through quasi-steady static coupling of aerodynamic data with a detailed vehicle dynamics model. The findings show that inside bias operating states of the split rear wing and tilted rear wing concepts, which favor loads on the inside tires, can improve cornering velocities up to 0.5% and 2% compared to high symmetric operating states, respectively. Noteworthy, through effective distribution of aerodynamic loads, the inside bias operating states produce less downforce and drag, thereby reducing the propulsion power required to overcome drag by 15%–20%, depending on the cornering condition. The tilted rear wing concept demonstrates the highest AALB capability and most consistent response to its control strategy, accredited to its ability to generate vertical and horizontal aerodynamic force components. It can, therefore, achieve over 1% higher maximum cornering velocities compared to the split rear wing, while also offering efficiency benefits. Overall, the research highlights the effectiveness of AALB in improving cornering performance and efficiency, offering valuable insights for the development of advanced active aerodynamic solutions in automotive design and paving the way for future advancements in the field. [ABSTRACT FROM AUTHOR]

In order to improve the delivery efficiency of logistics, this paper proposed a vehicle routing problem with drone and time window considered by the time window, UAV (unmanned aerial vehicle) power change and multi-point continuous delivery of UAV. Then, this paper proposed a hybrid particle swarm optimization algorithm with local search to solve it. Based on the hybrid particle swarm optimization algorithm, the proposed algorithm transformed the problem solution space into the algorithm search space by constructing an efficient encoding and decoding strategy. Further, the proposed algorithm combined a single point insertion strategy, a vehicle replacement strategy and a UAV replacement strategy to form a local search strategy to improve the optimization ability. The experimental results show that the proposed model is more efficient than the model of pure vehicle distribution and saves 31.51% of the cost. The proposed algorithm is better than the four comparison algorithms, and its highest optimization rate is 82.08%. [ABSTRACT FROM AUTHOR]

Distinguishing identity-unrelated background information from discriminative identity information poses a challenge in unsupervised vehicle re-identification (Re-ID). Re-ID models suffer from varying degrees of background interference caused by continuous scene variations. The recently proposed segment anything model (SAM) has demonstrated exceptional performance in zero-shot segmentation tasks. The combination of SAM and vehicle Re-ID models can achieve efficient separation of vehicle identity and background information. This paper proposes a method that combines SAM-driven mask autoencoder (MAE) pre-training and background-aware meta-learning for unsupervised vehicle Re-ID. The method consists of three sub-modules. First, the segmentation capacity of SAM is utilized to separate the vehicle identity region from the background. SAM cannot be robustly employed in exceptional situations, such as those with ambiguity or occlusion. Thus, in vehicle Re-ID downstream tasks, a spatially-constrained vehicle background segmentation method is presented to obtain accurate background segmentation results. Second, SAM-driven MAE pre-training utilizes the aforementioned segmentation results to select patches belonging to the vehicle and to mask other patches, allowing MAE to learn identity-sensitive features in a self-supervised manner. Finally, we present a background-aware meta-learning method to fit varying degrees of background interference in different scenarios by combining different background region ratios. Our experiments demonstrate that the proposed method has state-of-the-art performance in reducing background interference variations. [ABSTRACT FROM AUTHOR]

To address the issue of false and missed detection of non-motorized vehicles due to the small size and obstructed vision in autonomous vehicle target detection, this research refines YOLOv4 basic algorithm to bolster the accuracy of non-motorized vehicle detection. The optimized algorithm streamlines the feature extraction process through a cross-stage connection, concurrently diminishing computational overhead and bolstering detection efficiency. Additionally, Convolutional Block Attention Module (CBAM) is embedded to increase effective feature weights and improve detection accuracy through channel and spatial attention weights. A non-motorized vehicle detection model is established based on anchor adaptive matching using a self-built non-motorized vehicle dataset. To verify the effectiveness of the model, the performance of the model is compared through ablation experiments. The results show that the proposed detection model substantially improves the detection and recognition performance of non-motor vehicles, effectively solve the problems of missed and false detections. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) faces security challenges in maintaining privacy due to the presence of open networks and diverse services. Ensuring privacy is essential in transportation networks to provide users with a long-lasting driving, navigation, and communication experience. In this paper, the proposed Privacy Recommending Data Processing Model (PRDPM) is deployed to handle the huge amount of data accumulated in this field. The proposed model adopts data processing techniques that are dependent on user demand and are influenced by either neighboring entities or service providers. The various application requirements are analyzed to minimize the potential privacy consequences. The data from various intervals are utilized to validate the parameters in the operational plane. Thus, data balancing is performed using plane differentiation to prevent privacy leaks in either of the vehicular services. This is useful for neighbors and infrastructures across various applications/users. [ABSTRACT FROM AUTHOR]

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Advanced finite element (FE) modeling and simulations were performed on vehicular crashes into a three-bar metal bridge rail (TMBR). The FE models of a sedan, a pickup truck, and a TMBR section were adopted in the crash simulations subject to Manual for Assessing Safety Hardware (MASH) Test Level 2 (TL-2) and Test Level 3 (TL-3) requirements. The test vehicle models were first validated using full-scale physical crash tests conducted on a two-bar metal bridge using a sedan and a pickup truck with similar overall physical properties and sizes to their respective vehicles used in the simulations. The validated vehicular models were then used to evaluate the crash performance of the TMBR using MASH evaluation criteria for structural adequacy, occupant risk, and post-impact trajectory. The TMBR met all MASH TL-2 requirements but failed to meet the MASH TL-3 Criteria H and N requirements when impacted by the sedan. The TMBR was also evaluated under in-service conditions (behind a 1.52 m wide sidewalk) and impacted by the sedan under MASH TL-3 conditions. The simulation results showed that the TMBR behind a sidewalk met all safety requirements except for the occupant impact velocity in the longitudinal direction, which exceeded the MASH limit by 3.93%. [ABSTRACT FROM AUTHOR]

To establish battery electric vehicles as an attractive alternative to internal combustion vehicles, charging times of 15 min or less are increasingly demanded. This is especially challenging for lower battery temperatures, as this exacerbates the risk of accelerated battery degradation due to lithium plating. Therefore, active battery heating is utilized in state-of-the-art electric vehicles. To evaluate the impact of such heating strategies at vehicle level, we deployed an electrochemical battery model coupled with a longitudinal vehicle dynamics model. Using anode potential control to prevent lithium plating, we assess the time-saving potential versus the energy cost of different preconditioning and fast-charging strategies. The results reveal substantial energy saving and charge speed increase potential through optimal charge-stop planning, preconditioning timing, cost-adjusted thermal management thresholds, and considering driving behavior. This emphasizes the need for advanced operation strategies, taking into account both battery-level electrical and thermal restrictions, as well as vehicle integration and route planning. [ABSTRACT FROM AUTHOR]

With the rapid advancement of autonomous driving technology, the recognition of vehicle lane-changing can provide effective environmental parameters for vehicle motion planning, decision-making and control, and has become a key task for intelligent vehicles. In this paper, an improved method for vehicle taillight detection and intent recognition based on YOLOv8 (You Only Look Once version 8) is proposed. Firstly, the CARAFE (Context-Aware Reassembly Operator) module is introduced to address fine perception issues of small targets, enhancing taillight detection accuracy. Secondly, the TriAtt (Triplet Attention Mechanism) module is employed to improve the model's focus on key features, particularly in the identification of positive samples, thereby increasing model robustness. Finally, by optimizing the EfficientP2Head (a small object auxiliary head based on depth-wise separable convolutions) module, the detection capability for small targets is further strengthened while maintaining the model's practicality and lightweight characteristics. Upon evaluation, the enhanced algorithm demonstrates impressive results, achieving a precision rate of 93.27%, a recall rate of 79.86%, and a mean average precision (mAP) of 85.48%, which shows that the proposed method could effectively achieve taillight detection. [ABSTRACT FROM AUTHOR]

Vehicle classification technology is one of the foundations in the field of automatic driving. With the development of deep learning technology, visual transformer structures based on attention mechanisms can represent global information quickly and effectively. However, due to direct image segmentation, local feature details and information will be lost. To solve this problem, we propose an improved vision transformer vehicle classification network (IND-ViT). Specifically, we first design a CNN-In D branch module to extract local features before image segmentation to make up for the loss of detail information in the vision transformer. Then, in order to solve the problem of misdetection caused by the large similarity of some vehicles, we propose a sparse attention module, which can screen out the discernible regions in the image and further improve the detailed feature representation ability of the model. Finally, this paper uses the contrast loss function to further increase the intra-class consistency and inter-class difference of classification features and improve the accuracy of vehicle classification recognition. Experimental results show that the accuracy of the proposed model on the datasets of vehicle classification BIT-Vehicles, CIFAR-10, Oxford Flower-102, and Caltech-101 is higher than that of the original vision transformer model. Respectively, it increased by 1.3%, 1.21%, 7.54%, and 3.60%; at the same time, it also met a certain real-time requirement to achieve a balance of accuracy and real time. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office 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 study explores the effectiveness of vertical force control in in-wheel motors (IWMs) to enhance ride comfort in electric vehicles (EVs). A dynamic vehicle model and a proportional ride-blending controller were used to reduce vertical vibrations of the sprung mass. By converting the state-space model into a transfer function, the system's frequency response was evaluated using road profiles generated according to ISO 8608 standards and converted into Power Spectral Density (PSD) inputs. The frequency-weighted acceleration ( a w ) was calculated based on ISO 2631 standards to measure ride comfort improvements. The results showed that increasing the proportional gain ( K p ) effectively reduced the frequency-weighted acceleration and the RMS of the vertical acceleration of the sprung mass. However, the proportional gain could not be increased indefinitely due to the torque limitations of the IWMs. Optimal proportional gains for various road profiles demonstrated significant improvements in ride comfort. This study concludes that advanced suspension technologies, including the proportional ride-blending controller, can effectively mitigate the challenges of increased unsprung mass in IWM vehicles, thereby enhancing ride quality and vehicle dynamics. [ABSTRACT FROM AUTHOR]

Traditional vehicle fault diagnosis methods rely heavily on the expertise of mechanics or diagnostic tools available at service centers, which can be costly, time-consuming, and may not always provide accurate results. This study presents a comprehensive vehicle fault diagnosis framework, which utilized Mel-Frequency Cepstral Coefficients (MFCCs), Discrete Wavelet Transform (DWT)-based features, and the Extreme Learning Machine (ELM) classifier. To address the limitations of previous works, the proposed framework leverages a large, diverse dataset encompassing various vehicle models and real-world operating conditions. Significantly improved robustness and generalizability of the fault diagnosis system were achieved. The results of the experiments demonstrate the superiority of the MFCC-based features combined with the ELM classifier, achieving the highest performance metrics in terms of accuracy, precision, recall, F1-score, macro F1-score, and weighted F1-score, which are 92.17%, 92.24%, 92.22%, 92.10%, and 92.06%, respectively. Slightly lower performance was obtained while employing the DWT-based features compared to employing MFCC-based features. Additionally, frequency analysis was conducted to identify specific frequency bins, which are the most indicative of different fault types in providing valuable guidance for future diagnostic efforts. Overall, the proposed framework provides a reliable and practical solution for accurate vehicle fault detection, paving the way for future advancements in automotive diagnostics. [ABSTRACT FROM AUTHOR]

In the automotive sector, the use of multi-body software for modeling of existing vehicles has become essential due to its advantages in understanding vehicle dynamics in different situations and improving vehicle performances. This paper aims to model an off-road truck (4 × 4) by using ADAMS software 2020. Several steps must be achieved, including experimentally identifying some truck characteristics such as the mass, center of gravity coordinates, and tire vertical stiffness. The truck features leaf springs in both the front and rear suspensions, which must be validated before their integration into the full model due to their modeling complexity. This validation is performed by comparing the force–displacement characteristics obtained experimentally with simulation results from ADAMS, showing a good agreement. Then, the full truck is modeled in ADAMS software and validated through an experimental test using a repeated double-lane change scenario with two tests for the validation of the truck's lateral dynamics. The comparison between the results shows a good correlation, validating the multi-body truck model. [ABSTRACT FROM AUTHOR]

This paper presents a design method of a suspension controller with an adaptive feedforward algorithm for ride comfort enhancement and motion sickness mitigation. Recently, it was shown that motion sickness is caused by combined heave and pitch motions of a sprung mass within the range of 0.8 and 8 Hz. For this reason, it is necessary to design a suspension controller for the purpose of reducing the heave and pitch vibration of a sprung mass within this range. To represent the heave acceleration and the pitch rate of a sprung mass, a 4-DOF half-car model is adopted as a vehicle model. For easy implementation in a real vehicle, a static output feedback control is adopted instead of a full-state one. To reduce the heave acceleration of a sprung mass for ride comfort enhancement, a linear quadratic SOF controller is designed. To reduce the pitch rate of a sprung mass for motion sickness mitigation, a filtered-X LMS algorithm is applied. To validate the method, simulation on vehicle simulation software is conducted. From the simulation results, it is shown that the proposed method is effective for ride comfort enhancement and motion sickness mitigation. [ABSTRACT FROM AUTHOR]

Background: Concerns about the sustainability of commercially available batteries have driven the development of post-lithium systems. While previous studies on Magnesium batteries have explored both the potential environmental footprint of battery production and their possible use in stationary applications, their environmental impact in electromobility remains unexplored. This study provides an initial prospective evaluation of the environmental performance of a theoretical Mg–S battery for potential use in electric vehicles (EVs). Utilizing life cycle assessment (LCA) methodology, various scenarios are analyzed and compared to conventional systems. The analysis focuses on potential environmental impacts, including climate change, resource criticality, acidification of the biosphere, and particulate matter emissions. Results: In the battery pack level, the Magnesium anode and its respective supply chain have been identified as main drivers of environmental burdens. Additional concerns arise from the uneven geographical distribution of Mg production, which leads to dependency on few producers. In terms of resource criticality, the Mg–S battery could carry significant advantages over benchmark systems. A look into the use-phase via theoretical implementation in an electric vehicle (EV) also suggests that the Magnesium based EV could perform on a comparable level to an LIB EV, also outperforming conventional ICEVs in several impact categories. Conclusions: This study is based on optimistic assumptions, acknowledging several remaining technical challenges for the Mg battery. Consequently, the results are indicative and carry a significant degree of uncertainty. Nonetheless, they suggest that the Mg–S system shows promising environmental sustainability performance, comparable to other reference systems. [ABSTRACT FROM AUTHOR]

Providing safe, smooth, and efficient trajectories for autonomous vehicles has long been a question of great interest in the field of autopiloting. In dynamic and ever-changing urban environments, safe and efficient trajectory planning is fundamental to achieving autonomous driving. Nevertheless, the complexity of environments with multiple constraints poses challenges for trajectory planning. It is possible that behavior planners may not successfully obtain collision-free trajectories in complex urban environments. Herein, this paper introduces spatio–temporal joint optimization-based trajectory planning (SJOTP) with multi-constraints for complex urban environments. The behavior planner generates initial trajectory clusters based on the current state of the vehicle, and a topology-guided hybrid A* algorithm applied to an inflated map is utilized to address the risk of collisions between the initial trajectories and static obstacles. Taking into consideration obstacles, road surface adhesion coefficients, and vehicle dynamics constraints, multi-constraint multi-objective coordinated trajectory planning is conducted, using both differential-flatness vehicle models and point-mass vehicle models. Taking into consideration longitudinal and lateral coupling in trajectory optimization, a spatio–temporal joint optimization solver is used to obtain the optimal trajectory. The simulation verification was conducted on a multi-agent simulation platform. The results demonstrate that this methodology can obtain optimal trajectories safely and efficiently in complex urban environments. [ABSTRACT FROM AUTHOR]