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To solve the problem of trajectory tracking lateral control in autonomous driving technology, a model predictive control (MPC) controller trajectory tracking lateral control method combined with a deep deterministic policy gradient algorithm (DDPG) is proposed in this paper. This method inputs the real-time state of the vehicle into DDPG to achieve real-time automatic optimization of the prediction time domain and control time domain parameters of the MPC controller, and then affects the specific performance of the MPC controller in trajectory tracking lateral control. Specifically, the state space, action space, and reward function of DDPG are defined, and the automatic driving trajectory tracking lateral controller is designed in combination with the vehicle dynamics model. To reduce the exploration space of DDPG and improve the training efficiency of the entire model, the technique of advantage-disadvantage experience separation and extraction is introduced. Finally, the proposed method was trained and verified in various scenarios, and compared with two other lateral control methods for autonomous driving. The results showed that the learning and training time of the trajectory tracking lateral control method based on DDPG-MPC was shorter than that of the DDPG-based method, and the evaluation indicators in the trajectory tracking control process were better than those of the DDPG-based method and original MPC-based method. [ABSTRACT FROM AUTHOR]

To increase vehicle dynamics modelling accuracy in simulation environments, in this work, we propose a model, where the mutual influence of the vehicle dynamics and tyre wear is taken into account. In this context, simulation-oriented models aim at representing the main features of the vehicle dynamics, while keeping the complexity low, making them suitable for control-oriented analyses and design. The wear model quantifies tyre wear as a tyre mass loss depending on the tyre-road interaction, derived from the vehicle dynamics and kinematics; then, the outcome of this model is consequently used to update the tyre friction characteristics, by modifying the main parameters of the Pacejka's magic formula. We also discuss the steps required to use the model in two-wheeled vehicles, where the roll dynamics plays an important role in the tyre wear. The main features of the developed model are discussed on a motorcycle, as a simulation case-study. [ABSTRACT FROM AUTHOR]

In a typical traffic scenario, automated vehicles are required to interact with surrounding traffic participants, e.g. human-driven vehicles, pedestrians, etc. In this paper, a novel adaptive authority allocation strategy considering the social behaviours of surrounding vehicles is proposed for the shared steering control (SSC) of automated vehicles. First, a Koopman-based potential-field-driven distributed model predictive control (K-PF-DMPC) method is proposed for the modelling of vehicle interaction to describe surrounding vehicle's social behaviour. This method effectively deals with the nonlinearity embedded in the non-cooperative game-based vehicle interaction model, so that the analytical form of Nash equilibrium can be derived for a fast online solution. Then, the SSC system of the automated vehicle is implemented by a weighted summation method. To balance the driving performance and intervention degree while considering the surrounding vehicles' social behaviours, a hybrid fuzzy strategy (HFS) is proposed for allocating the control authority between the driver and automation. The value of authority allocation coefficient is calculated by fusing the outputs from two shared fuzzy controllers, i.e. the enhanced and weakened shared fuzzy controllers, in accordance to the belief of the surrounding vehicle's type. Several numerical simulations and driver-in-the-loop simulator experiments are conducted for validation. Results show that the proposed strategy can provide appropriate steering intervention to the driver owing to the consideration of the surrounding vehicle's social behaviour and obtains the best driver-automation collaboration performance compared with the comparative groups. [ABSTRACT FROM AUTHOR]

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

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

This paper investigates the problem of tracking target control of a nonlinear system in the context of cyber-physical systems for connected autonomous vehicles with external unknown disturbances. A new robust tracking strategy via backstepping sliding-mode control is proposed. First, a connected nonlinear vehicle dynamical model with disturbances is constructed. Then, a disturbance observer is presented to approximate the unknown disturbances when the derivative of the disturbance is bounded. This paper has proved that the error of the observation can converge to zero in finite time. Third, a tracking control method is designed which combines the backstepping method with the sliding-mode method. According to the method, the estimated values of interference are used as a priori knowledge. Furthermore, the stability of the designed control strategy is demonstrated through using the Lyapunov theory. Finally, simulation experiments are presented to demonstrate the feasibility of the proposed approaches. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

Objective: Excessive skin exposure to deleterious environmental variables results in inflammation as well as molecular and cellular impairments that compromise its functionality, aesthetic qualities, and overall well‐being. The implementation of topical administration of antioxidants and other compounds as a method for preventing or reversing damage is a rational approach. Numerous phenolic compounds derived from plants have demonstrated capabilities such as scavenging free radicals and promoting tissue healing. However, the primary obstacle lies in effectively delivering these compounds to the specific place on the skin, and accurately forecasting their diffusion through the skin can assist in determining the most effective tactics. Hence, this article provides a comprehensive analysis of recent literature pertaining to the in vitro skin diffusion characteristics of plant phenolics. The aim is to gain a deeper understanding of their behaviour when present in various forms such as solutions, suspensions, and formulations. Method: The data on plant extracts and isolated plant phenolic compounds in vitro skin diffusion assays published over the last six years were compiled and discussed. Results: Even though the gold standard Franz diffusion cell is the most commonly used in the assessment of in vitro plant phenolic skin diffusion profiles, a plethora of skin models and assay conditions are reported for a variety of compounds and extracts in different vehicles. Conclusion: The presence of numerous models and vehicles poses a challenge in creating correlations among the existing data on plant phenolic compounds. However, it is possible to draw some general conclusions regarding molecular, vehicle, and skin characteristics based on the gathered information. [ABSTRACT FROM AUTHOR]

To investigate the vibration characteristics of off-road vehicles resulting from external excitations, this study explores a vibration modelling approach that accounts for the coupling of roll and pitch. A low-frequency vibration test was conducted, and the results were analysed in both the time domain and frequency domains. The mathematical model of the tractor was validated, affirming the accuracy of the off-road vehicle modelling method. This study focuses on the Shandong Wuzheng Group Co., Ltd. Renoman 2104 ratio tractor prototype (1:18) as the research subject. A vibration mathematical model and simulation model were developed, taking into account the coupled vibrations in both roll and pitch directions. Key parameters of the tractor, including damping, stiffness, centroid, inertia, and road roughness, were acquired through experimental testing. Upon comparing the numerical simulation model results with the experimental data, the findings reveal a 6.2% error between the left front axle test data and the simulation, and a 6.1% error for the right front axle. The vertical vibration primary frequency in the test is 13.1Hz, while the numerical simulation model registers 11.8 Hz, resulting in a relative error of 10%. Notably, the numerical simulation model exhibits significant consistency with the experimental results. These outcomes serve as a valuable reference for the parameter design and vibration control of off-road vehicles. • A vibration modelling approach of off-road vehicles is proposed. • The coupling vibration of pitch and roll is considered. • The methodology for acquiring tractor dynamic parameters has been elucidated. • An analysis of both time domain and frequency domain results was conducted to validate the mathematical model of the tractor. [ABSTRACT FROM AUTHOR]

The purpose of this study is to extend the mileage by improving the efficiency of the electric vehicle (EV) driving and thermal management system (TMS). The powertrain system and the TMS must be considered simultaneously from the architecture stage as complementary systems to maintain the driving performance of the EV. This paper proposes an EV architectural design method and a multi-physical EV modeling technique. The main components of the EV are standardized differently from those of the internal combustion engine (ICE) vehicle, and the TMS is designed by thermal system configuration. Therefore, instead of the traditional continuous variable-based design methodology, optimal combination design based on the component database (DB) and the configuration plot presents the combinational design method. The performance of an EV varies depending on the performance of driving components and TMS, and weather. This paper presents a Modelica-based vehicle modeling technique to integrate multiple physical phenomena. Finally, we implemented an automated design using the optimal combination design method and the multi-physical integrated vehicle model design framework. The design of the C-segment EV was carried out to review the validity of the proposed method. 2880 laboratories were planned and designed using design variables with 10 levels of mixing. As a result of the design, we have confirmed that the mileage increases by 5% compared to the previous one in NEDC mode. [ABSTRACT FROM AUTHOR]

The application of 'Target Cascading' in the product development process is well known to be effective in various industry fields, and it is a methodology that is already actively applied in various forms in the field of vehicle Ride & Handling (R&H) performance development. This paper proposes a model-based vehicle performance benchmarking methodology and a technology for setting system characteristics to achieve handling performance objectives as a means of utilization for this. Rather than introducing already well-known target deployment techniques, a process is proposed for practical use in the vehicle development phase through the development of interfaces between low degrees of freedom vehicle model and optimization software. The methodology presented in this study is expected to be available in a timely manner as a practical vehicle performance development tool. [ABSTRACT FROM AUTHOR]

A comprehensive RFD-FTC-DCA system is proposed for the hypersonic vehicle with saturation constraints to resist the control surface damage, actuator faults, disturbances and parameter perturbation. Firstly, the hypersonic vehicle fault model is established. Secondly, the robust fault detection (RFD) observer is designed to detect the control surface damage and actuator faults accurately under the influences of disturbances and parameter perturbation. Thirdly, the fault-tolerant control (FTC) law is designed to resist the control surface damage, actuator faults, disturbances and parameter perturbation simultaneously. Finally, a dynamic control allocation (DCA) algorithm is designed to reallocate the FTC law and limit the deflection angle and velocity within saturation constraint. The comparison simulation results are presented to demonstrate that the RFD observer can detect the faults accurately and reduce false alarm rates effectively, the FTC law can resist the control surface damage, actuator faults, disturbances and parameter perturbation simultaneously, and the tracing errors of attack angle, side-slip angle and roll angle are only $ \pm 0.7^\circ $ ± 0.7 ∘ , $ \pm 0.2^\circ $ ± 0.2 ∘ and $ \pm 1.5^\circ $ ± 1.5 ∘ , respectively, and the DCA algorithm can reallocate the FTC law and limit the deflection angle and velocity within saturation values. [ABSTRACT FROM AUTHOR]

We present a general approach to model the motion of a rigid body ground vehicle on a smooth nonplanar road surface. We generalise planar control-oriented models while preserving standard model states, inputs and outputs. We derive our approach from first principles, apply it to existing vehicle modelling approaches and test it through real-world experiments. We demonstrate that our approach improves upon the prediction accuracy of planar models. [ABSTRACT FROM AUTHOR]

In this paper, a typical motion-based modelling and tracking issue is investigated for vehicle targets in linear road segment by a variable structure multiple model (VSMM) method. Vehicle target motions in a road with multiple lanes are described by typical lane keeping and lane changing manoeuvres. To describe trajectories of typical manoeuvres, an Ornstein–Uhlenbeck process and a sine half-cycle are used to model lane keeping and lane changing motions in lateral direction, respectively. Note that starting point and length of lane changing motion are unknown in target tracking. Time-varying model sets are designed based on the current motion model with different motion parameters. A VSMM tracking frame is constructed to obtain target state estimates with time-varying model set. The effectiveness of the proposed typical motion-based tracking scheme is displayed by simulation results on road vehicle tracking. [ABSTRACT FROM AUTHOR]

This paper optimizes the assignment of shared automated vehicles under users' uncertain departure times. Automated vehicles can drive themselves, so no staff are needed to relocate vehicles in the one-way carsharing system. To optimize fleet placement and use, a twophase solution method was established. Phase 1 strategically distributes vehicles across stations using a system optimization approach, while Phase 2 tracks vehicle movements via an agent-based simulation model. Phase 1 solutions serve as inputs to Phase 2 simulations. Using a fleet size of roughly 10,000 vehicles, case study applications were run across the Austin, Texas region's six-county network. In the base case setting, results suggest that system profits are optimized when vehicle rental is priced at $1.28=km ($0.8=mi). The number of proactive vehicle relocations falls 9.8% if the relocation operation cost rises from $0.096=km ($0.06=mi) to $0.32=km ($0.2=mi). Average per-trip profit is $10.60 when using high-cost vehicles, and $11.60 when using low-cost vehicles. Results from a 3-h simulation show an average person-trip length of 25 km (15.6 mi), with 29.6 min of average driving time. When a 24-h day was simulated, the vehicle-occupied time and vehicle-distance traveled were 4 h and 200 km (125 mi) per vehicle-day, respectively. The low coefficient of variation of satisfied demand across 30 demand scenarios suggests the robustness of the two-phase solution method. [ABSTRACT FROM AUTHOR]

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

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

Purpose: The serious wear problem of heavy-haul freight vehicle wheels affects the safety and economy of vehicle operation. This paper aims to study wheel wear evolution law and the influence of line parameters on wheel wear of heavy-haul freight, and provide the basis for operation and line maintenance. Design/methodology/approach: The wheel wear test data of heavy-haul freight vehicles were analyzed. Then a heavy-haul freight vehicle dynamic model was established. The line parameters influencing wheel wear in heavy-haul freight vehicles were also analyzed by the Jendel wear model, and the effects of rail cant, rail gauge, rail profile and line ramp on wheel wear were analyzed. Findings: A rail cant of 1:40 results in less wheel wear; an increase in the rail gauge can reduce wheel wear; and when matched with the CHN60 rail, the wear depth is relatively small. A decrease of 9.21% in wheel wear depth when matched with the CHN60 rail profile. The ramp of the heavy-haul line is necessary to consider for calculating wheel wear. When the ramp is considered, the wear depth increases by 8.47%. The larger the ramp, the greater the braking force and therefore, the greater of the wheel wear. Originality/value: This paper first summarizes the wear characteristics of wheels in heavy-haul freight vehicles and then systematically analyzes the effect of line parameters on wheel wear. In particular, this study researched the effects of rail cant, rail gauge, rail profile and line ramp on wheel wear. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0038/ [ABSTRACT FROM AUTHOR]

Intelligent vehicles require accurate identification of traversable road areas and the ability to provide precise and real-time localization data in unstructured road environments. To address these issues, we propose a system for traversable map construction and robust localization in unstructured road environments based on a priori knowledge. The proposed method performs traversable area segmentation on the LiDAR point cloud and employs a submap strategy to jointly optimize multiple frames of data to obtain a reliable and accurate point cloud map of the traversable area, which is then rasterized and combined with the vehicle kinematic model for global path planning. Then, it integrates priori map information and real-time sensor information to provide confidence and priori constraints to ensure the robustness of localization, and it fuses multi-sensor heterogeneous data to improve real-time localization. Experiments are conducted in a mining environment to evaluate the performance of the proposed method on an unstructured road. The experimental results demonstrate that the traversable map and localization results based on the proposed method can meet the requirements for autonomous vehicle driving on unstructured roads and provide reliable priori foundation and localization information for autonomous vehicle navigation. [ABSTRACT FROM AUTHOR]

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

This study investigates the particle dispersion characteristics in the turbulent wake of a simplified vehicle model (Ahmed body) for two values of the rear slant angle ϕ (25 ° and 40 °) to study the effect of flow separation. In the experiments (Reynolds number Rel=1.90 × 10 5 ), smoke particles were released from a source and visualized with a laser sheet. Concentration fields were analyzed to calculate the vertical (Sy) and lateral (Sz) smoke spread. The findings indicate that the flow topology and concentration fields in the wake are highly dependent on ϕ. In the ϕ = 40 ° case, separation on the rear slant disrupts the trailing vortices originating on the rear slant edges and significantly alters the wake topology. The growth of vertical smoke spread saturates after the recirculation region. The uniform mixing and absence of trailing vortices concentrate the smoke particles in the model midplane. In the ϕ = 25 ° case, the signatures of trailing vortices were observed in the concentration fields behind the model. The vertical smoke spread is less, and the lateral smoke spread is more compared to the ϕ = 40 ° case. The growth of the smoke spread ( S y , S z ) driven by the trailing vortices persists for a long distance, even after the recirculation region. The disruption of trailing vortices brought about by the flow separation appears to be an important effect driving the vertical smoke spread in the wake. The connection between the turbulent velocity structures and concentration structures will need to be explored with combined velocity and concentration measurements in the wake. [ABSTRACT FROM AUTHOR]

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

Within the urban road system, traffic conflicts most frequently are concentrated at road intersections, and left-turning traffic is a prominent and pivotal factor leading to intersection conflicts. The scarcity of applied models that can comprehensively elucidate the shifting trajectory dynamics and exit lane selection behavior of left-turning vehicles has posed challenges to the accurate analysis and prediction of intersection vehicle conflict patterns. This study extracted trajectories of left-turning vehicles at intersections from video data. A vehicle left-turn trajectory model was formulated, hinging on changes in trajectory curvature. Moreover, leveraging geometric intersection parameters and video data, a vehicle exit lane selection model was devised using random utility theory. This model integrates variables such as intersection turning angle, vehicle type, entrance lane position, vehicle speed, following clearance, and the lead vehicle’s position in the exit lane. The outcomes indicate that left-turn trajectories can be approximated well with a flat curve, and the trajectory’s curvature has a quadratic parabolic correlation with the intersection’s aspect ratio. The trajectory model presented in this paper adeptly characterizes left-turning vehicle movement across diverse intersections. Similarly, the exit lane selection model adeptly forecasts the distribution of left-turning vehicles in exit lane positions. This predictive efficacy stems from the recognition that turning angle, entrance lane vehicle position, and vehicle speed profoundly impact the exit lane choice for left-turning vehicles. This research sheds light on the intricacies of driving behavior and exit lane selection of left-turning vehicles at signalized intersections. By focusing on these critical aspects, this study offers valuable insights that contribute to the improvement of intersection design and overall traffic safety. [ABSTRACT FROM AUTHOR]

This work investigates commercial vehicle platoon predictive cruise control for highways. We propose a cloud-based platoon predictive cruise control method (CPPCC). A two-layered control architecture of the CPPCC is proposed as a platoon predictive cruise speed planning layer in the cloud and a platoon stabilization control layer. The CPPCC communication topology is proposed to achieve coupled control of the hierarchical architecture. The speed planning layer is a dynamic planning (DP) algorithm based on road slope in the rolling distance domain. The lower layer is a stability control algorithm to meet the stability requirements of vehicle platoon driving; the vehicle side is distributed model predictive control (DMPC). The CPPCC is validated by real road and vehicle data models, and comparative experiments with the traditional predecessor-leader following–cruise control (PLF-CC) platoon and predecessor following–cruise control (PF-CC) platoon. The speed error of the vehicle platoon was maintained at [−0.25, 0.30] (m=s) and the space error at [−0.13, 0.66] (m) in platoon stability. Against the comparison method, the CPPCC saved fuel by over 5.13% and achieved an overall operational efficiency improvement of 5.71%. [ABSTRACT FROM AUTHOR]

Countdown timers impact driver behavior significantly and are widely used at signalized intersections in many countries. Literature shows potential vehicle class-specific temporal and spatial variations in driver behavior in the presence of timer. Therefore, first, this paper analyzed the temporal, spatial, and vehicle class-specific impacts of timer on driver behavior in heterogeneous traffic conditions based on variations in acceleration. Second, a Long Short-Term Memory model was developed to model vehicle discharge, incorporating the heterogeneity of traffic and impacts of timer. The performance of the model was compared with that of the Intelligent Driver Model using field trajectory data from three signalized intersections in Kerala, India. The model accurately predicts the following behavior with mixed spacing errors of 0.24 to 0.35 and an average mean squared error of spacing of 1.23. The results were better than, or comparable to, results from recent data-driven car-following models and significantly better than IDM. [ABSTRACT FROM AUTHOR]

We present a Vehicle Model (VM) that has 17 degrees of freedom and includes nonlinear tire and powertrain subsystems. Implemented as a relatively small piece of C++ code, the model runs vehicle dynamics 2000 times faster than real time at a simulation time step of $1 \times 10^{-3}, \text {s}$ on a single core of a commodity CPU. When executed on the GPU, one can perform 300000 vehicle simulations in real-time. These properties make the model a good candidate for reinforcement learning, model predictive control, model predictive path integral control, path planning, state estimation, and traffic simulation tasks. The model is expressive in that it can capture the dynamics of vastly different vehicles. This is demonstrated by first calibrating the model to mimic the dynamics of a 1/ $6^{th}$ scale vehicle called the Autonomy Research Testbed (ART) vehicle, which has a mass of approximately 5.8 kg. Subsequently, the model is calibrated to mimic the dynamics of a heavy-duty High Mobility Multipurpose Wheeled Vehicle (HMMWV), which has a mass of 2097 kg. The Bayesian calibration process discussed can $(i)$ handle real-life measurement noise, and $(ii)$ provide model parameter probability distributions. The vehicle model, which is open source and freely available in a public repository, can also be imported into Python owing to SWIG wrapping.

Multi-intelligent vehicles forming and maintaining a specific formation during traveling can save energy consumption and road resources, and improve traffic efficiency and driving safety. Centered around the Cooperative Vehicle Infrastructure(CVI) framework of the Internet of Vehicles(IOV), this paper delves into the control objectives of forming multi-intelligent vehicle formation, explores the theory of formation shaping process, and investigates the optimization of energy consumption in the formation shaping process. First, the clustering design is used to take the kinematic process of vehicles as an optimization process from the bionics, taking the instantaneous consumption indexes as the optimization objectives. Then, optimize the acceleration inputs of the vehicle formation process using the improved Artificial Fish Swarm Algorithm(AFSA), and iterate with the clustering algorithm part, so as to realize the formation shaping process with energy consumption as the optimization objective. Finally, ajoint simulation platform built by Simulation of Urban Mobility (SUMO) and Python is used for simulation verification, and the results show that the formation shaping process can be realized and achieve the optimal energy consumption under the scenarios proposed in this paper, thus verifying the validity of the clustering model of vehicle formation shaping and the energy consumption optimization algorithm designed in this paper. [ABSTRACT FROM AUTHOR]

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

Four-wheel steering (4WS) is an effective technique to improve handling performance and lateral stability of road vehicles. Conventionally, controllers utilise the driver's actions and vehicle dynamics to coordinate front and rear-axle steering. This paper proposes a novel approach for 4WS controller design, based on the concept of Artificial Flow Guidance (AFG), which relies on a spatially distributed motion reference through a two-dimensional vector field. This field provides high-level guidance while lower-level steering controllers to control axle centres motions relative to the flow. These flow vectors, computed in real-time via simple geometric construction, can be pre-computed globally to evaluate the guidance algorithm's efficacy. When controlling only the front axle, this same approach can function as an autonomous driving system. Relying solely on a spatial reference field and control targets' velocities enables the controller to work in a simple and robust fashion, without using a reference vehicle dynamics model or lengthy parameter tuning. The proposed approach's effectiveness is validated through co-simulation with MATLAB/Simulink and the CarMaker simulation platform. AFG control performance is found to be at least comparable to that of more complex 4WS controllers using methods such as MPC; in the cases considered, AFG provides superior path-tracking performance. [ABSTRACT FROM AUTHOR]

The paper presents a novel control strategy for autonomous vehicles, which can effectively handle the nonlinear effects and uncertainties of the controlled system. This solution is based on a new formulation of the ultra-local model-based control approach. This method can deal with the unknown dynamics and other uncertainties of the vehicle without any further measurement or complex modelling process. The control methods require a reduced-complexity nominal linear model of the system, and the unknown part is continuously estimated by the proposed ultra-local model. The proposed control-oriented vehicle modelling method leads to an increased performance level of vehicle motion. The effectiveness of the proposed method through a high-fidelity vehicle simulator (CarMaker) is illustrated. Moreover, the operation of the control through its implementation on a test vehicle of ThyssenKrupp is also demonstrated. [ABSTRACT FROM AUTHOR]

Semi-active primary suspensions are an effective means of improving ride quality in high-speed railway vehicles in relation to the mitigation of car-body bending vibration. In this paper, prototype magnetorheological (MR) dampers are tested and the results are used to define a mathematical model of the dampers. Then, three control schemes for semi-active primary suspensions are proposed: Skyhook, LQG and Mix-1-Sensor, and their performance is assessed by means of Hardware-In-the-Loop (HIL) tests considering a simple quarter-vehicle model which is run on a real-time board and set in interaction with one physical MR damper. The results show that all three considered control strategies lead to a reduction of car-body vibration by around 30% and a very good agreement is found between HIL tests and numerical simulations in which the physical damper is replaced by the mathematical damper model. The damper model is finally interfaced with a flexible multi-body model of the complete vehicle to provide further assessment of semi-active control. The results of the latter simulations show that the semi-active suspension could provide an improvement of the $ {N_{mvz}} $ N mvz ride quality index in the order of 40–45% with respect to the passive vehicle for all three control schemes. [ABSTRACT FROM AUTHOR]

Polygonalization is a typical non-uniform wear phenomenon that affects the performance and comfort of the vehicle/track system. Therefore, the vehicle system performance could be strictly troubled by polygonal damage close to the wheel circumference at the wheel/rail contact. A 3D vehicle/track an EMU type train consisting of 8 coupled vehicles model has been established. The coupled model car body and bogie frame are considered rigid and integrated with the flexible wheelset, rail, and slab track to investigate the elastic deformation caused by the impact load induced by the wheel polygonalization. The flexible track can be incorporated into the multi-body vehicle model using the movable reference frame formulation. Uncertainty parameters are presented, such as passenger weight, wind force, coefficient friction, and suspension variation. The proposed research is validated based on the existing research work and shows excellent agreement. The simulation result deliberated the vertical wheel-rail force, wheelset acceleration, rail acceleration, rail displacement, and slab track acceleration. The results of this study show that as the wheel polygonal damage amplitude increase, the value of wheel/rail normal forces and the dynamic vibrational response of the wheelset axle increase. Additionally, polygonal wear-induced impact stresses may cause the wheelset axle to resonate. When wheel polygonal damage amplitude and uncertainty parameters are corporate into the system dynamics, the maximum magnitude of the wheelset, rail, and slab track acceleration increases. Besides, the train failure degree is comparable to the polygonised wheel harmonic order, defect parameters, and amplitude of out-of-roundness. [ABSTRACT FROM AUTHOR]

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

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

The control goal of the vehicle platoon is to maintain the same speed and desired distance. Most current studies are based on simplified vehicle models, and the leader's state is also rarely considered. However, under complex working conditions, such as low adhesion or curves, the lateral stability of the platoon will be difficult to guarantee, and tracking errors of desired speed and spacing may further increase. To solve the above problems, a new hierarchical coordinated control strategy is proposed. Taking distributed drive electric vehicles (DDEVs) as research objects, the upper control level establishes a stability situation assessment model according to the vehicle's dynamic characteristics. At the medium control level, variable weight model predictive control (MPC) coordinates conflicts between longitudinal tracking and lateral stability. A correction term is also introduced to revise the prediction model. At the same time, the weight of different control objectives of the leader and following vehicle was adjusted, respectively. Torque distribution is carried out at the lower level controller. Finally, the control strategy is tested on a hardware-in-the-loop (HIL) platform. The results show that the proposed control strategy can ensure lateral stability while improving the tracking performance of the vehicle platoon. [ABSTRACT FROM AUTHOR]

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