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The algorithm function designed in this paper can make a car maintain stability during automatic vehicle hold through the model input of multi-level target fluid pressure combined with slope judgment modules of different levels after the automatic vehicle hold software works. At the same time, a complete parking function module is designed, which can monitor the whole parking process in real time. Through the design of this function, the functional diversity of the electro-hydraulic braking system can be increased. When judging that the driver intends to start, the automatic vehicle hold system will automatically release the fluid pressure according to the opening of the accelerator pedal pressed by the driver so that the vehicle does not happen to brake when the vehicle starts in the slippery slope condition. Finally, real vehicle verification proves that the function can effectively meet the parking requirements and start on the flat and on a ramp. Also, it can effectively control the vehicle according to the driver's driving intention. [ABSTRACT FROM AUTHOR]

In this paper, the authors investigate the issue of constructing and incorporating an accurate vehicle dynamic model for model predictive control (MPC) with an application to autonomous vehicle path following. To achieve the desired performance, MPC employs a precise dynamic model. However, the uncertainty of complex systems and their operating environments presents a challenge to the development of an adequately accurate vehicle dynamic model. This paper proposes a Deep Gaussian Process Regression (DGPR) method to improve model precision. Meanwhile, the learning model is incorporated into a novel MPC framework to enhance closed‐loop performance. High‐fidelity simulations using CarSim‐MATLAB demonstrate the validity of the proposed approach in terms of enhancing the path following performance and lateral stability under the condition of large curvature at medium to high speeds on roads with different friction coefficients when compared to the nominal MPC approach. [ABSTRACT FROM AUTHOR]

Network synthesis has been well developed for the linear suspensions system, but it is not applicable to nonlinear ones with the mem-inerter. Based on the equivalent inertance theorem of the mem-inerter, this work designs a new suspension structure by proposing a network synthesis that is applicable to the nonlinear suspension systems, which includes structure synthesis and parameter synthesis. Overall, the new suspension systems show superior performance. In terms of the structure synthesis, the suspension network is taken as a robust controller, and thus the suspension system with the mem-inerter is obtained by solving the controller. For parameter synthesis, the multi-objective parameter optimization method is used to determine the parameters of the system, and the assembly parameters are calculated according to the actual engineering situation. Besides, a quarter-car vehicle model based on the network synthesis is built, and the load adaptability of vehicles is evaluated with three suspension structures under different loads. Compared with the suspension with the inerter, this design offers much better ride comfort and handling stability, demonstrating the necessity and benefit of introducing network synthesis to nonlinear suspension with mem-inerter. [ABSTRACT FROM AUTHOR]

The high-speed collision issue brought on by railway vehicles' intensive and high-speed operation has become a critical concern for operational safety. Therefore, it is necessary to research the high-speed collision problem of trains. Firstly, a train collision dynamic model suitable for higher collision velocities is established. This model includes a 38-degree-of-freedom (DOF) three-dimensional vehicle model and a three-layered vibrating fixed-track model. Corresponding mathematical models are developed for nonlinear factors such as wheel–rail contact, car–end contact, and coupler overload representation in the model. Then, the Train Collision State Score (TCSS) is proposed, enabling quantitative safety evaluation under different collision conditions. Finally, the influence of the energy-absorbing device's initial attitude and leading car parameters on collisions is studied. The results indicate that initial vertical height difference and pitching angle significantly increase TCSS. As the force level of the energy-absorbing device of the leading car increases, the effects of two intermediate coupler overload states on TCSS show opposite trends. [ABSTRACT FROM AUTHOR]

Model-predictive control (MPC) offers significant advantages in addressing constraint-related challenges and plays a pivotal role in self-driving car technology. Its primary goal is to achieve precise trajectory tracking while prioritizing vehicle stability and safety. However, real-time operations often face challenges related to computational demands and low computational efficiency. To address these challenges, this paper introduces a novel lateral control algorithm for self-driving vehicles, which utilizes the linear complementarity problem (LCP) instead of the conventional quadratic programming (QP) method as the MPC optimization solution. This innovative approach incorporates the electric steering system into the vehicle dynamics model, allowing for precise torque regulation of the steering motor and enhancing control accuracy. The MPC algorithm adopts the LCP solution method to calculate control signals based on the vehicle's state, ensuring both rapid and stable vehicle control. Simulation results demonstrate that the proposed MPC algorithm, utilizing the LCP solution method, effectively addresses efficiency issues in the lateral motion of self-driving cars. This leads to improvements in both driving stability and real-time performance. Overall, this innovative approach lays a solid foundation for the practical implementation of self-driving cars. [ABSTRACT FROM AUTHOR]

During the car production process, diverse production workshops have distinct prerequisites for car body sequencing. This results in the intricate nature of sequencing within multi-stage car workshops. In this study, an optimization method for car body sequencing is proposed that combines a hybrid evolutionary algorithm with heuristic rules. In the welding workshop, a genetic algorithm is employed to optimize the vehicle sequencing. Simultaneously, a differential evolution algorithm is used to optimize the inbound sequence of the buffer zone between the welding and painting workshops, as well as the inbound sequence of the buffer zone between the painting and assembly workshops. Heuristic rules are applied to optimize the outbound sequence of the buffer zone between the welding and painting workshops, as well as the outbound sequence of the buffer zone between the painting and assembly workshops. In addition, in order to improve the quality of the initial population, a heuristic method-based initial population construction method is proposed. The optimization objectives are the number of vehicle model changes in the welding workshop, the number of color changes in the painting workshop, and the total number of overloads in the assembly workshop. The experimental results show that the proposed method performs better than the five outstanding evolutionary algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China 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.)

Vehicle re-identification identifies target vehicles using images obtained by numerous non-overlapping real-time surveillance cameras. The effectiveness of re-identification is further challenging because of illumination changes, pose differences of captured images, and resolution. Fine-grained appearance changes in vehicles are recognized in addition to the coarse-grained characteristics like color of the vehicle along with model, and other custom features like logo stickers, annual service signs, and hangings to overcome these challenges. To prove the efficiency of our proposed bipartite attention framework, a novel dataset called Attributes27 which has 27 labelled attributes for each class are created. Our framework contains three major sections: The first section where the overall and semantic characteristics of every individual vehicle image are extracted by a double branch convolutional neural network (CNN) layer. Secondly, to identify the region of interests (ROIs) each branch has a self-attention block linked to it. Lastly to extract the regional features from the obtained ROIs, a partition-alignment block is deployed. The results of our proposed system’s evaluation on the Attributes27 and VeRi-776 datasets has highlighted significant regional attributes of each vehicle and improved the accuracy. Attributes27 and VeRi-776 datasets exhibits 98.5% and 84.3% accuracy respectively which are comparatively higher than the existing methods with 78.6% accuracy. [ABSTRACT FROM AUTHOR]

Summary: Electric vehicles (EVs) are drastically growing, and their emergence is more popular. Moreover, with the constraint driving range, the batteries of these vehicles are rechargeable throughout the routes in various situations. Here, a new problem is formulated with the inclusion of both battery swapping and partial recharging decisions. The energy level and the routing directions are more important, and the investigators intend to handle these issues effectually. However, the major constraint is routing and time window. Here, a heuristic model (LTS‐RSS) is designed to find the best matching solution. A novel Random Sub‐Space (RSS) and local Tabu search (LTS) are modeled to handle these issues. The probabilistic model of RSS is anticipated by integrating the consequences of time windows and distances. The experimentation is done with an online database and used for performance validation. The outcomes show that the newly modeled (LTS‐RSS) approach enhances the significance of the model. The outcomes of all the instances with diverse strategies enhance the model's robustness and stability for resolving these issues. The empirical analysis is done with MATLAB 2020b simulator, and metrics like optimization of routing solution, the best vehicle, best distance, and numbers of vehicles are evaluated, and the outcomes are compared with various other approaches. [ABSTRACT FROM AUTHOR]

In emergency scenarios, lane changing can provide a considerable advantage over braking by aiding in the prevention of rear-end collisions. However, executing lane changes on horizontal curves might lead to rollover collisions. This study proposes a systematic methodology for quantifying the rollover safety margin during lane-changing maneuvers by encompassing the complex characteristics of vehicle-road interactions. Specifically, an enhanced six-degree-of-freedom vehicle dynamics model was developed for a tractor-semitrailer and integrates road superelevation. Using this model, the rollover safety margin reduction rate (fS) was calculated. The fS represents the ratio of the difference between the lateral load transfer ratio margins under both reference state and emergency lane change conditions to the lateral load transfer ratio margin in the reference state. The reference state corresponds to vehicles maintaining 80 km·h-1 on a 270 m radius curve, while the emergency condition is defined as lane change durations of less than 4 seconds. The results reveal that emergency lane change maneuvers and roadway alignment significantly affect rollover safety margin. Shorter lane change duration, higher speed, and smaller radius worsen the rollover safety margin; these effects are further amplified when the lane change direction is opposite to the curve's bending direction. When the tractor-semitrailer performs a lane change at 60 km·h-1 within a 4-second duration on a 600 m radius curve, the fS exceeds 100%, indicating an imminent rollover. Consequently, this study contributes valuable evidence to the development of more reliable and secure lane-change strategies. [ABSTRACT FROM AUTHOR]

The subject of electric vehicles (EVs) is constantly relevant from the perspective of climate change and sustainability. Multi-Criteria Decision Analysis (MCDA) methods can be successfully used to evaluate models of such vehicles. In many cases, the MCDA methods are modified to account for uncertainty in the data. There are many ways to express uncertainty, including more advanced ones, such as fuzzy sets, for example, but expressing attributes in terms of interval numbers remains a popular method because it is an easy-to-implement and easy-to-understand technique. This study focuses on interval extensions of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. It aims to compare the most popular extension proposed by Jahanshahloo and the proposed new modification, which returns the result in an interval form. Certain inconsistencies of the Jahanshahloo extension are discussed, and it is explained how the new extension avoids them. Both extensions are applied to an EV evaluation problem taken from the literature as an example for sustainable assessment. The results are then analyzed, and the question of whether the input data of the interval should receive an evaluation in the form of interval results is addressed. [ABSTRACT FROM AUTHOR]

Vehicle make and model recognition (VMMR) is an important aspect of intelligent transportation systems (ITS). In VMMR systems, surveillance cameras capture vehicle images for real-time vehicle detection and recognition. These captured images pose challenges, including shadows, reflections, changes in weather and illumination, occlusions, and perspective distortion. Another significant challenge in VMMR is the multiclass classification. This scenario has two main categories: (a) multiplicity and (b) ambiguity. Multiplicity concerns the issue of different forms among car models manufactured by the same company, while the ambiguity problem arises when multiple models from the same manufacturer have visually similar appearances or when vehicle models of different makes have visually comparable rear/front views. This paper introduces a novel and robust VMMR model that can address the above-mentioned issues with accuracy comparable to state-of-the-art methods. Our proposed hybrid CNN model selects the best descriptive fine-grained features with the help of Fisher Discriminative Least Squares Regression (FDLSR). These features are extracted from a deep CNN model fine-tuned on the fine-grained vehicle datasets Stanford-196 and BoxCars21k. Using ResNet-152 features, our proposed model outperformed the SVM and FC layers in accuracy by 0.5% and 4% on Stanford-196 and 0.4 and 1% on BoxCars21k, respectively. Moreover, this model is well-suited for small-scale fine-grained vehicle datasets. [ABSTRACT FROM AUTHOR]

Remaining driving range (RDR) research has continued to consistently evolve with the development of electric vehicles (EVs). Accurate RDR prediction is a promising approach to alleviate distance anxiety when power battery technology is not yet fully matured. This paper first introduces the research motivation of RDR prediction, summarizes the previous research progress, and classifies the influencing factors of RDR. Second, conduct research and analysis on the physical model of EVs, mainly including battery and vehicle models. Based on the physical model, the energy flow problem of EVs is analyzed and discussed. Third, four key challenges of RDR prediction are summarized: battery state estimation, driving behavior classification and recognition, driving condition prediction and speed prediction, and RDR calculation method. Finally, given the four challenges faced by RDR, a driving range prediction method based on vehicle‐cloud collaboration is proposed, which combines the advantages of cloud computing and machine learning to provide further research trends. [ABSTRACT FROM AUTHOR]

The control barrier function (CBF) provides a good tool to design supervisory controllers for safety‐critical systems. However, constructing a CBF for a general nonlinear system is a challenging task. This paper presents a general CBF computational synthesis approach for nonlinear affine systems with state constraints and input constraints using sum‐of‐squares (SOS) programming. System dynamics and state constraints are approximated using polynomial functions, and the input constraints are approximated using linear functions. The CBF synthesis algorithm is presented step‐by‐step, and tricks to deal with potential numerical issues during the SOS optimizations are discussed. The proposed algorithm is applied to an automotive lane keeping application to synthesize a CBF, and a supervisory control based on this CBF is designed. The effectiveness and resilience of this CBF‐based supervisory control against disturbance are illustrated in simulation with a 14‐degree‐of‐freedom vehicle dynamics model. Results show that this CBF‐based supervisory control can improve the resilience of nominal controllers in automotive lane keeping applications. [ABSTRACT FROM AUTHOR]

Track irregularity is the main factor that affects the safety and comfort of high-speed railway. It is important to study the influence of the track irregularity on the safety service of high speed train–track coupling system. In this paper, a vehicle–track spatial nonlinear coupled system dynamics model is developed with FEM. The model includes vehicle subsystem and track subsystem, which are coupled by wheel–rail interface. The vehicle subsystem is a 31-DOF spatial vehicle model, and the track subsystem is a three-dimensional slab track model composed of the rail, rail pad and track slab. Considering wheel tread profile and rail head profile, the wheel–rail spatial contact geometry relationship is established, and the "Trace method" and the "Minimum distance method" are used to search for wheel–rail contact points. Considering the nonlinear contact between the wheel and the rail, a numerical approach is presented, where the "Trace method" is embedded into the algorithm for solving the vehicle–track nonlinear coupling dynamics equation by cross iteration, realizing searching the wheel–rail contact points and solving the vehicle–track nonlinear coupling equation simultaneously, which greatly improves the efficiency of numerical analysis. As application examples, the dynamic responses of a high speed train–track spatial nonlinear coupled system excited by the track V-shaped local profile irregularity and the track torsional irregularity are analyzed. The results show that the algorithm is effective in solving the vehicle–track spatial nonlinear coupled system dynamics problem, and the track irregularity has significant influence on the dynamic response of vehicle and track structure. [ABSTRACT FROM AUTHOR]

Fine-grained image recognition aims to classify fine subcategories belonging to the same parent category, such as vehicle model or bird species classification. This is an inherently challenging task because a classifier must capture subtle interclass differences under large intraclass variances. Most previous approaches are based on supervised learning, which requires a large-scale labeled dataset. However, such large-scale annotated datasets for fine-grained image recognition are difficult to collect because they generally require domain expertise during the labeling process. In this study, we propose a self-supervised transfer learning method based on Vision Transformer (ViT) to learn finer representations without human annotations. Interestingly, it is observed that existing self-supervised learning methods using ViT (e.g., DINO) show poor patch-level semantic consistency, which may be detrimental to learning finer representations. Motivated by this observation, we propose a consistency loss function that encourages patch embeddings of the overlapping area between two augmented views to be similar to each other during self-supervised learning on fine-grained datasets. In addition, we explore effective transfer learning strategies to fully leverage existing self-supervised models trained on large-scale labeled datasets. Contrary to the previous literature, our findings indicate that training only the last block of ViT is effective for self-supervised transfer learning. We demonstrate the effectiveness of our proposed approach through extensive experiments using six fine-grained image classification benchmark datasets, including FGVC Aircraft, CUB-200-2011, Food-101, Oxford 102 Flowers, Stanford Cars, and Stanford Dogs. Under the linear evaluation protocol, our method achieves an average accuracy of 78.5 % , outperforming the existing transfer learning method, which yields 77.2 % . [ABSTRACT FROM AUTHOR]

In this paper, a system identification methodology based on vehicle response and test track prioritization is presented. The proposed method can be used to perform time-domain parameter estimation by simply driving the vehicle on different road profiles, thus eliminating the necessity of having a special test setup or excitation input feed. The method is based on exploiting the dependence of the identifiability of vehicle parameters on vehicle responses and test tracks. To prioritize the test tracks, the responses and test tracks are weighed based on their capabilities to estimate the vehicle parameters, and these weights are obtained in a preliminary analysis of a seven-degree-of-freedom ride model developed in MATLAB® (R2021b). An optimization framework based on a differential evolution scheme with an optional archive scheme (JADE) is used to estimate the twelve vehicle parameters. The proposed system identification scheme is verified on tests using a high-fidelity vehicle model developed in ADAMS® (29.2)using several tracks with different severities. Experimental validation is performed on a test vehicle, and it is demonstrated that the vehicle suspension system parameters can be identified accurately with fast convergence performance. [ABSTRACT FROM AUTHOR]

The accelerating progress of the Internet of Vehicles (IoV) has put forward a higher demand for distributed model training and data sharing in vehicular networks. Traditional centralized approaches are no longer applicable in the face of drivers' concerns about data privacy, while Decentralized Federated Learning (DFL) provides new possibilities to address this issue. However, DFL still faces challenges regarding the non-IID data of passing vehicles. To tackle this challenge, a novel DFL framework, Hierarchical Decentralized Federated Learning (H-DFL), is proposed to achieve qualified distributed training among vehicles by considering data complementarity. We include vehicles, base stations, and data center servers in this framework. Firstly, a novel vehicle-clustering paradigm is designed to group passing vehicles based on the Bloom-filter-based compact representation of data complementarity. In this way, vehicles train their models based on local data, exchange model parameters in each group, and achieve a qualified local model without the interference of imbalanced data. On a higher level, a local model trained by each group is submitted to the data center to obtain a model covering global features. Base stations maintain the local models of different groups and judge whether the local models need to be updated according to the global model. The experimental results based on real-world data demonstrate that H-DFL dose not only reduces communication latency with different participants but also addresses the challenges of non-IID data in vehicles. [ABSTRACT FROM AUTHOR]

Road excitation is the basic input of vehicle vibration, which greatly impacts on vehicle ride comfort. In addition, it can cause vertical vibration of the vehicle, leading to deformation of the powertrain mount. Similarly, the longitudinal movement of the powertrain can also cause deformation of the powertrain mount. Due to the effect of the powertrain mount, there is a mutual coupling between the vehicle in the longitudinal and vertical directions, which has a great influence on the smoothness and comfort of the vehicle. In order to reveal the impact of random road excitation on the vehicle dynamic characteristics of a dual clutch transmission (DCT) vehicle under starting and shifting conditions, a longitudinal-vertical coupled dynamic model of the DCT vehicle considering the powertrain mount was established. This model includes dynamic torque model of the engine, powertrain mount model, transmission system model, tire model and road excitation model. The influence of random road excitation on the vehicle's dynamic characteristics is analyzed under starting, shifting, and different starting and shifting intentions. The simulation results of the longitudinal-vertical coupling dynamic DCT vehicle model considering road excitation are compared with vehicle test results, the model is found to accurately reflect the influence of road excitation on the dynamic performance of DCT vehicles under starting and shifting conditions, thus verifying the correctness of the model. [ABSTRACT FROM AUTHOR]

Fuel-saving-oriented collaborative driving is a highly promising yet challenging endeavor that requires satisfying the driver's operational intentions while surpassing the driver's fuel-saving performance. In light of this challenge, the paper introduces an innovative collaborative driving strategy tailored to the objective of fuel conservation in the context of commercial vehicles. An enhancement to this strategy involves the development of a network prediction model for vehicle speed, leveraging insights from driver style recognition. Employing the predicted speed as a reference, a model-predictive-control-based optimal controller is designed to track the reference while optimizing fuel consumption. Furthermore, a straightforward yet effective collaborative rule is proposed to ensure alignment with the driver's intention. Subsequently, the proposed control scheme is validated through simulation and real-world driving data, revealing that the human–machine cooperative driving controller saves 4% more fuel than human drivers. [ABSTRACT FROM AUTHOR]

Relocating the electric motor to a position inside the wheel offers a variety of benefits, such as reduced overall vehicle weight, more efficient production of vehicles, more flexible design and better road handling because of the accurate torque control on each wheel. However, the motor also increases the unsprung mass, which can impair the ride comfort of the vehicle. In this paper, we propose and investigate a control strategy using the vertical component of the driving force provided by an in-wheel motor for better ride comfort and handling dynamics. In the next stage, a mathematical model of the vehicle with coupled longitudinal, lateral, and vertical dynamics was introduced. The parameters of the vehicle were measured and collected from an actual car equipped with in-wheel motors. To analyze the performance of the controller, a model of the vehicle was tested in a series of simulations. The straight-line acceleration test indicated an improvement to the ride comfort of 31.8% in terms of the RMS vertical vibrations of the sprung mass. The control is achieved without any deterioration of the tire load and the vehicle's longitudinal dynamics. The stable lateral dynamics of the controlled vehicle was confirmed using a sine-sweep steering excitation, conducted on a perfectly smooth road surface with a constant longitudinal acceleration. [ABSTRACT FROM AUTHOR]

A growing number of studies have demonstrated the effectiveness and superiority of reverse engineering in using axle box vibration acceleration signals to deduce the operation state of wheelsets. However, there is a lack of discussion and summary on the overall mapping relationship between wheel excitation and axle box response, and there has been no detailed investigation on resonance characteristics of axle box. To address the deficiency, a novel resonant excitation loading method is proposed, which is applied to the vehicle model with flexible wheelset and axle box. Amplitude frequency response (AFR) curve of the resonance characteristics of axle box is obtained. AFR is used to characterise the characteristics of various excitation states of wheel, including wheel polygon, wheel tread flat, and wheel-rail separation state, and the corresponding evolution law is revealed. Finally, through two case studies, the guiding role of axle box resonance characteristics on wheel operation status detection is discussed in detail. Analyses show that mastering the resonance characteristics of axle box is an effective way to deduce the operation status of wheel based on the complex axle box vibration response signal, and it will provide a basis for the quantitative evaluation of wheel local damage. [ABSTRACT FROM AUTHOR]

This paper aims at developing a novel detection and identification method against malicious attacks in intelligent transportation. Due to the development and applications of communication and advanced sensor technologies, intelligent transportation has faced new safety risks. In particular, the emerging malicious attacks, such as false data injection attack, can mask the destruction of physical dynamic by tampering with information in layer to fool the current detection methods. Because of this reason, an adaptive unknown input observer-based detection and identification method is developed. Firstly, a physical dynamics model of vehicle networking system is established by considering the actual physical state. Considering the spoofing characteristics of false data injection attack, an unknown input observer-based detection method is proposed. Through the design of adaptive unknown input observer parameters, the detection performance, can be improved by cutting down the state estimation error. Compared with the UIO-based detection method, simulations demonstrate that the false positive rate can be reduced 0.1%. Based on the feature of state residuals that is not sensitive to the attacked i th residual, but sensitive to other residuals, a novel identification criterion is developed. At last, simulation experiments on the Matlab verify the performance of the proposed detection and identification algorithm in intelligent transportation system. [ABSTRACT FROM AUTHOR]

In recent years, since the unique advantages in automotive structures, the vehicle active suspension systems have received widespread attentions. A good active suspension system can reduce the vibration and improve the overall performance of the vehicle. Therefore, the design of the controller for the active suspension system to perform autonomous adjustment plays a vital role in vehicle comfort and safety. For the active suspension of the seven-DOF sport utility vehicle (SUV) model, this paper takes the vehicle body acceleration, tire dynamic load and suspension dynamic travel as the indicators to evaluate the performance, and the proportional-integral-derivative (PID) controller is designed to improve the performance of the vehicle active suspension system. Based on the software of MATLAB/Simulink and Carsim, a closed-loop co-simulation model diagram is established, which includes a PID controller module. Meanwhile, the random road input model and the whole vehicle model are constructed in Carsim. Finally, at the speeds of 70, 90, and 120 km/h, the active suspension system under the designed PID controller is simulated and compared with the passive suspension system. The simulation results show that the active suspension system based on PID controller can effectively improve the overall performance of the vehicle, and then the comfort and safety of the vehicle can be further enhanced. [ABSTRACT FROM AUTHOR]

Polymer nanocomposites (PNCs) usually have superior properties than pristine polymers. Understanding the dynamics properties in PNC system is crucial to reveal the mechanism of property change unpon the addition of nanoparticles (NPs), and therefore for a better design of the material properties. In this short perspective, we summarize recent advances mainly from theoretical and simulation studies of dynamics properties in polymer nanocomposite system. One is the "vehicle model" which reveals that diffusion dynamics of sticky NP is coupled to surrounding chain segments. Similarly, recent simulations demonstrate that such coupling also exists in all-polymer nanocomposite wich is composed of linear polymer chains and single-chain nanoparticles (SCNPs). These SCNPs have almost the same chemical composition as the matrix chain. Therefore, it is assumed that such all-polymer nanocomposite can act as a model system where there are no enthalpic interactions between NPs and polymer chains. Although the above dynamic coupling was found in the above two different systems containing inorganic NPs or relatively small organic SCNPs, it was found that the length scale of such dynamic coupling (the thickness of the matrix/NP interface) is comparable to the NP size, which is surprisingly consistent in the above two different systems. In addition, a chain-length dependence of the NP influence on the chain dynamics reported from a recent joint simulaiton and experimental study of all-polymer nanocomposite system, and a theoretical model developed for such phenomena are also reviewed. At the end, we give an outlook of this field, especially for possible chain-length dependence of complex dynamics in sticky-NP systems. [ABSTRACT FROM AUTHOR]

Vehicle active safety system is the hotspot of current research and development, and the road adhesion coefficient is one of the essential parameters in the development of automobile active safety system, which plays a pivotal role in automobile safety. To improve the real-time performance and accuracy of the road adhesion coefficient, this paper proposes different control methods and compares and analyzes them. To accurately estimate the road adhesion coefficient in real-time, an eight-degree-of-freedom vehicle model was adopted in this paper. According to the tire torque and wheel speed, the adhesion coefficient was estimated based on the second-order nonlinear extended state observer. The experimental data, such as slip rate, using the improved tire model, is based on the recursive least squares method with forgetting factor and fuzzy logic algorithm real-time of tire-road friction coefficient estimation. The results show that the observer can effectively observe utilisation adhesion coefficient, and both control algorithms can estimate the road adhesion coefficient in real-time, effectively and quickly. On different roads, the use of the recursive least squares algorithm resulted in higher estimation accuracy, better robustness, and better control effect than using a fuzzy logic algorithm. [ABSTRACT FROM AUTHOR]

• Derive new closed-form solution for multi contact responses from a six-DOF vehicle model. • Identify track modulus from the first "plus" frequency of the dual-beam with a simple formula. • Detect rail damage from the instantaneous amplitude squared generated from the driving component of the contact response. • Contact-point spectrum performs better since it is free of the vehicle frequencies. • Parametric study for factors affecting the technique. The vibration data recorded for the test vehicle cannot be directly used to detect the rail and bridge frequencies due to the overshadowing effect of vehicle's frequencies. To overcome this problem, the vehicle-rail contact response may be used instead. However, current techniques on this are available only for simple test vehicles with single or two degrees of freedom (DOFs). Normally, a train car contains a car body and two bogies (each of two wheelsets), and should be modeled by a multi- DOF system. The objective of this paper is first to solve the four wheelset-rail contact responses from the six measured vehicle responses. Then the contact responses are employed to retrieve the track modulus , while detecting the rail damages, for their salient feature of being free of the vehicle's frequencies. The railway track is modeled by a dual-beam system, with the upper (rail) and lower (bridge) beams connected by a spring-dashpot layer. The track modulus is retrieved through identification of the first "plus" frequency of the dual-beam, and the rail damage is detected from the instantaneous amplitude squared (IAS) generated from the driving component of the contact response. Through comparison with results by the finite element method (FEM), it is confirmed that: (1) the proposed procedure for computing the multi contact responses from the multi-DOF vehicle responses is reliable; (2) track irregularity presents a limited influence even for high disturbance; (3) to trade-off between efficiency and accuracy, a medium vehicle speed is suggested for the field measurement; (4) for the dual-beam with sufficient length, the use of simply supported condition is acceptable for rails; and (5) the effect of track damping is small and can be ignored. [ABSTRACT FROM AUTHOR]

The highlight model is widely used as a simple and convenient method in the radar field but its accuracy is not high. Based on the traditional highlight model, the algorithm has been improved to address the acoustic scattering problems of underwater vehicles with more complex line shapes. The basic idea is to partition the model into micro-bodies to calculate the scattered sound pressure, consider the phase interference of each part, and then synthesise the scattered sound pressure to approximate the target's actual shape. A computational model of the wedge-shaped convex structure on the back of the underwater vehicles is developed using a highlight model of a trapezoidal plate. The results of the calculations using the highlight model approach are consistent with those of the planar element method. Utilising the modified highlight model method, the accuracy of acoustic scattering characteristics calculations for the stern and overall structures of underwater vehicles has proven satisfactory. Finally, fast acoustic scattering prediction software is developed for underwater vehicles, enabling the calculation of the acoustic scattering characteristics for individual structures, combined structures, and coated silent tiles. This software provides algorithmic support for the fast prediction of the acoustic stealth performance of underwater vehicles. [ABSTRACT FROM AUTHOR]

Based on the cognitive characteristics of drivers in L2-L3 level intelligent vehicles under the condition of human-vehicle co-driving, a stress steering model for drivers was established under the emergency conditions of human-vehicle co-driving intelligent vehicles. The steering characteristics of drivers in the overstressed state were reflected by the model. The degree of driver stress was characterized by the fitting relationship between steering wheel angle amplitude and steering frequency in the stress steering curve. Considering the body roll under high speed and large turning angles, a nonlinear tire model was introduced to establish three-degree-of-freedom nonlinear vehicle dynamics model. The driver stress steering model was verified by tracking the simulation of lateral acceleration. The results indicate that drivers in the state of excessive stress can cause excessive front wheel turning of vehicle. The model output results are well consistent with the actual vehicle test results, and the model can describe the driver stress steering characteristics. [ABSTRACT FROM AUTHOR]

Fatigue vehicle load is essential for fatigue analysis, whose accuracy is directly associated with fatigue design. With the development of society, traditional fatigue vehicle load may fail to reflect modern traffic conditions, especially for transportation hubs in developed areas. Based on traffic flow data of a long-span steel box girder bridge, statistical analysis is performed and the two types of standard vehicle load are equivalent. Further, fatigue characteristics and fatigue life of two types of welded connection are compared based on the equivalent structural stress method. As a result, 2-axle vehicle takes the largest proportion, and the overload ratio of a 6-axle vehicle is the highest. Compared with fatigue vehicle load III, there should be a correction coefficient 1.89 and 2.30 for equivalent vehicle load based on average traffic flow and heavy lane traffic flow, respectively. For a single-side welded connection and a double-side welded connection, the equivalent structural stress will increase by 81.0%–131.2% for positions of concern when considering heavy lane traffic flow, which is about 21% higher than results considering average traffic flow. In addition, the fatigue life of these concerned positions will encounter a dramatic decrease. Fatigue life of the most unfavorable position is 492.4 years and cracks will occur in 90.0 years based on fatigue vehicle load III. However, it will decrease to 42.3 years and 7.7 years, respectively, when considering the traffic flow of the heavy lane. [ABSTRACT FROM AUTHOR]

This paper presents the utilization of robust nonlinear control schemes for a single-rotor unmanned aerial vehicle (SR-UAV) mathematical model. The nonlinear dynamics of the vehicle are modeled according to the translational and rotational motions. The general structure is based on a translation controller connected in cascade with a P-PI attitude controller. Three different control approaches (classical PID, Super Twisting, and Adaptive Sliding Mode) are compared for the translation control. The parameters of such controllers are hard to tune by using a trial-and-error procedure, so we use an automated tuning procedure based on the Particle Swarm Optimization (PSO) method. The controllers were simulated in scenarios with wind gust disturbances, and a performance comparison was made between the different controllers with and without optimized gains. The results show a significant improvement in the performance of the PSO-tuned controllers. [ABSTRACT FROM AUTHOR]

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

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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.)

The driving smoothness of vehicles is heavily influenced by their suspension system, and implementing active suspension control can effectively minimize the vibration movement of the vehicle and ensure a comfortable driving experience. An 8-DoF active suspension model of the full vehicle is established, and a fuzzy-PID controller is designed to autonomously regulate the parameters of the PID controller. Using the MATLAB/Simulink environment, a simulation model for suspension is created, and the vibration characteristics of passive, PID control, and fuzzy-PID control suspensions are compared with the help of the continuous crossing road hump model and C-level road model as road inputs. The results show that the utilization of fuzzy-PID control considerably diminishes the vertical, pitch, and roll oscillations of the suspension body and modifies the suspension dynamic deflection and tire dynamic load in contrast to the other two scenarios, thus enhancing ride comfort. Fuzzy-PID control led to a decrease of approximately 40% in acceleration, 25% in suspension workspace, and 30% in tire deflection compared to passive suspension. In addition, the reduction in acceleration is about 20%, the reduction in suspension workspace is approximately 10%, and the reduction in tire deflection is about 15% compared to the PID control suspension system. [ABSTRACT FROM AUTHOR]

When an intelligent vehicle changes lanes, the state of other vehicles may change, which increases the risk of collision. Therefore, real-time local path replanning is needed at this time. Based on model predictive control (MPC), a lane-change trajectory replanning strategy was proposed, which was divided into a lane-change trajectory correction strategy, a lane-change switchback strategy and forward active collision avoidance strategy according to collision risk. Based on the collision risk function of the rectangular safety neighborhood, the objective functions were designed according to the specific requirements of different strategies. The vehicle lateral controller based on MPC and the vehicle longitudinal motion controller were established. The longitudinal velocity was taken as the joint point to establish the lateral and longitudinal integrated controller. The trajectory planning module, trajectory replanning module and trajectory tracking module were integrated in layers, and the three trajectory replanning strategies of lane-change trajectory correction, lane-change switchback and forward active collision avoidance were respectively simulated and verified. The simulation results showed the trajectory replanning strategy achieves collision avoidance under different scenarios and ensures the vehicle's driving stability. The trajectory tracking layer achieves accurate tracking of the conventional lane-change trajectory and has good driving stability and comfort. [ABSTRACT FROM AUTHOR]

Copyright of Operations Research Transactions / Yunchouxue Xuebao is the property of Editorial office of Operations Research Transactions 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.)

Since large articulated vehicles have uncertainties in trailer articulation angle as well as dynamic complexity, it is not easy to accurately establish a reliable motion plan. In this paper, two geometric path plans constructed based on the empirical rules of driving experts are presented so that articulated vehicles can automatically perform perpendicular parking on a reverse path. By analyzing the empirical parking methods of professional drivers, these path plans were constructed by appropriately combining several standardized simple basic motions to facilitate implementation in real vehicles. In addition, the path plans included appropriate complementary motions to effectively respond to uncertainties arising from articulation angles, etc. The complementary motions developed in this study are based on the results of qualitative analysis on the behavior of articulated vehicles. The usefulness of the proposed articulated vehicle parking method has been proven through hundreds of experimental tests using a scaled model automated vehicle. [ABSTRACT FROM AUTHOR]

Computer vehicle simulators are used to model real-world situations to overcome time and cost limitations. The vehicle simulators provide virtual scenarios for real-world driving. Although the existing simulators precisely observe movement on the basis of good-quality graphics, they focus on a few driving vehicles instead of accident simulation. In addition, it is difficult to represent vehicle collisions. We propose a vehicle crash simulator with simulation and animation components. The proposed simulator synthesizes and simulates models of vehicles and environments. The simulator animates corresponding to the simulation through the execution results. The simulation results validate that the proposed simulator provides collision and non-collision results according to the speed of two vehicles at an intersection. [ABSTRACT FROM AUTHOR]

The ride comfort provided by a vehicle to the driver and the passengers is an important feature, directly correlated to the technical characteristics of the suspension system of the vehicle. In the literature, several lumped-parameter models simulating the vehicle and the driver are proposed for the computational evaluation of ride comfort. In order to quantify ride comfort, other than the values of acceleration, metrics such as seat effective amplitude transmissibility (SEAT) and seat-to-head transmissibility (STHT) are utilized. In this paper, a quarter car model is coupled with a six-degree-of-freedom lumped-parameter model, consisting of the driver's seat and the driver. A sensitivity analysis is performed on the values of the lumped parameters of the seated human body with regard to ride comfort in order to evaluate the effect of their accuracy relative to the ride comfort evaluation. The results of the sensitivity analysis revealed that the values of the mass, the stiffness and the damping parameters of the seated human model influence the ride-comfort metrics to a different extent. Furthermore, it was depicted that ride-comfort metrics were affected in different manners depending on the characteristics of the excitation of the vehicle, yet less than 10% Finally, the importance of the consideration of single-disturbance excitations in such sensitivity studies emerged. [ABSTRACT FROM AUTHOR]