Vehicles最新文献_第4页

Planning Speed Mode of All-Wheel Drive Autonomous Vehicles Considering Complete Constraint Set 考虑完整约束集的全轮驱动自动驾驶汽车速度模式规划

Vehicles Pub Date : 2024-01-12 DOI: 10.3390/vehicles6010008

M. Diachuk, Said M. Easa

{"title":"Planning Speed Mode of All-Wheel Drive Autonomous Vehicles Considering Complete Constraint Set","authors":"M. Diachuk, Said M. Easa","doi":"10.3390/vehicles6010008","DOIUrl":"https://doi.org/10.3390/vehicles6010008","url":null,"abstract":"The study aims to improve the technique of motion planning for all-wheel drive (AWD) autonomous vehicles (AVs) by including torque vectoring (TV) models and extended physical constraints. Four schemes for realizing the TV drive were considered: with braking internal wheels, using a rear-axle sport differential (SD), with braking front internal wheel and rear-axle SD, and with SDs on both axles. The mathematical model combines 2.5D vehicle dynamics model and a simplified drivetrain model with the self-locking central differential. The inverse approach implies optimizing the distribution of kinematic parameters by imposing a set of constraints. The optimization procedure uses the sequential quadratic programming (SQP) technique for the nonlinear constrained minimization. The Gaussian N-point quadrature scheme provides numerical integration. The distribution of control parameters (torque, braking moments, SDs’ friction moment) is performed by evaluating linear and nonlinear algebraic equations inside of optimization. The technique proposed demonstrates an essential difference between forecasts built with a pure kinematic model and those considering the vehicle’s drive/control features. Therefore, this approach contributes to the predictive accuracy and widening model properties by increasing the number of references, including for actuators and mechanisms.","PeriodicalId":509694,"journal":{"name":"Vehicles","volume":" 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139623836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collision Risk in Autonomous Vehicles: Classification, Challenges, and Open Research Areas 自动驾驶汽车的碰撞风险：分类、挑战和开放研究领域

Vehicles Pub Date : 2024-01-12 DOI: 10.3390/vehicles6010007

Pejman Goudarzi, Bardia Hassanzadeh

引用次数: 0

Highly Discriminative Driver Distraction Detection Method Based on Swin Transformer 基于斯温变换器的高度鉴别驾驶员分心检测方法

Vehicles Pub Date : 2024-01-10 DOI: 10.3390/vehicles6010006

Ziyang Zhang, Lie Yang, Chen Lv

{"title":"Highly Discriminative Driver Distraction Detection Method Based on Swin Transformer","authors":"Ziyang Zhang, Lie Yang, Chen Lv","doi":"10.3390/vehicles6010006","DOIUrl":"https://doi.org/10.3390/vehicles6010006","url":null,"abstract":"Driver distraction detection not only helps to improve road safety and prevent traffic accidents, but also promotes the development of intelligent transportation systems, which is of great significance for creating a safer and more efficient transportation environment. Since deep learning algorithms have very strong feature learning abilities, more and more deep learning-based driver distraction detection methods have emerged in recent years. However, the majority of existing deep learning-based methods are optimized only through the constraint of classification loss, making it difficult to obtain features with high discrimination, so the performance of these methods is very limited. In this paper, to improve the discrimination between features of different classes of samples, we propose a high-discrimination feature learning strategy and design a driver distraction detection model based on Swin Transformer and the highly discriminative feature learning strategy (ST-HDFL). Firstly, the features of input samples are extracted through the powerful feature learning ability of Swin Transformer. Then, the intra-class distance of samples of the same class in the feature space is reduced through the constraint of sample center distance loss (SC loss), and the inter-class distance of samples of different classes is increased through the center vector shift strategy, which can greatly improve the discrimination of different class samples in the feature space. Finally, we have conducted extensive experiments on two publicly available datasets, AUC-DD and State-Farm, to demonstrate the effectiveness of the proposed method. The experimental results show that our method can achieve better performance than many state-of-the-art methods, such as Drive-Net, MobileVGG, Vanilla CNN, and so on.","PeriodicalId":509694,"journal":{"name":"Vehicles","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EGFormer: An Enhanced Transformer Model with Efficient Attention Mechanism for Traffic Flow Forecasting EGFormer：用于交通流量预测的带有高效关注机制的增强型变压器模型

Vehicles Pub Date : 2024-01-06 DOI: 10.3390/vehicles6010005

Zhihui Yang, Qingyong Zhang, Wanfeng Chang, Peng Xiao, Minglong Li

{"title":"EGFormer: An Enhanced Transformer Model with Efficient Attention Mechanism for Traffic Flow Forecasting","authors":"Zhihui Yang, Qingyong Zhang, Wanfeng Chang, Peng Xiao, Minglong Li","doi":"10.3390/vehicles6010005","DOIUrl":"https://doi.org/10.3390/vehicles6010005","url":null,"abstract":"Due to the regular influence of human activities, traffic flow data usually exhibit significant periodicity, which provides a foundation for further research on traffic flow data. However, the temporal dependencies in traffic flow data are often obscured by entangled temporal regularities, making it challenging for general models to capture the intrinsic functional relationships within the data accurately. In recent years, a plethora of methods based on statistics, machine learning, and deep learning have been proposed to tackle these problems of traffic flow forecasting. In this paper, the Transformer is improved from two aspects: (1) an Efficient Attention mechanism is proposed, which reduces the time and memory complexity of the Scaled Dot Product Attention; (2) a Generative Decoding mechanism instead of a Dynamic Decoding operation, which accelerates the inference speed of the model. The model is named EGFormer in this paper. Through a lot of experiments and comparative analysis, the authors found that the EGFormer has better ability in the traffic flow forecasting task. The new model has higher prediction accuracy and shorter running time compared with the traditional model.","PeriodicalId":509694,"journal":{"name":"Vehicles","volume":"34 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139535863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Back Propagation Neural Network-Based Fault Diagnosis and Fault Tolerant Control of Distributed Drive Electric Vehicles Based on Sliding Mode Control-Based Direct Yaw Moment Control 基于滑动模式控制的直接偏航力矩控制--基于反向传播神经网络的分布式驱动电动汽车故障诊断与容错控制

Vehicles Pub Date : 2023-12-29 DOI: 10.3390/vehicles6010004

Tianang Sun, P. Wong, Xiaozheng Wang

{"title":"Back Propagation Neural Network-Based Fault Diagnosis and Fault Tolerant Control of Distributed Drive Electric Vehicles Based on Sliding Mode Control-Based Direct Yaw Moment Control","authors":"Tianang Sun, P. Wong, Xiaozheng Wang","doi":"10.3390/vehicles6010004","DOIUrl":"https://doi.org/10.3390/vehicles6010004","url":null,"abstract":"Distributed-drive vehicles utilize independent drive motors on the four-wheel hubs. The working conditions of the wheel-hub motors are so harsh that the motors are prone to failing under different driving conditions. This study addresses the impact of drive motor faults on vehicle performance, particularly on slippery roads where sudden faults can lead to accidents. A fault-tolerant control system integrating motor fault diagnosis and a direct yaw moment control (DYC) based fault-tolerant controller are proposed to ensure the stability of the vehicle during various motor faults. Due to the difficulty of identifying the parameters of the popular permanent magnet synchronous wheel hub motors (PMSMs), the system employs a model-free backpropagation neural network (BPNN)-based fault detector. Turn-to-turn short circuits, open-phase faults, and diamagnetic faults are considered in this research. The fault detector is trained offline and utilizes rotor speed and phase currents for online fault detection. The system assigns the torque outputs from both healthy and faulted motors based on fault categories using sliding mode control (SMC)-based DYC. Simulations with four-wheel electric vehicle models demonstrate the accuracy of the fault detector and the effectiveness of the fault-tolerant controller. The proposed system is prospective and has potential for the development of distributed electric vehicles.","PeriodicalId":509694,"journal":{"name":"Vehicles","volume":"96 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139146986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Battery Electric Vehicles: How Many Gears? A Technical–Economic Analysis 电池电动汽车：有多少齿轮？技术经济分析

Vehicles Pub Date : 2023-12-25 DOI: 10.3390/vehicles6010003

Emmanuele Bertucci, F. Bucchi, M. Ceraolo, Francesco Frendo, G. Lutzemberger

{"title":"Battery Electric Vehicles: How Many Gears? A Technical–Economic Analysis","authors":"Emmanuele Bertucci, F. Bucchi, M. Ceraolo, Francesco Frendo, G. Lutzemberger","doi":"10.3390/vehicles6010003","DOIUrl":"https://doi.org/10.3390/vehicles6010003","url":null,"abstract":"The large majority of electric cars have a single-speed gearbox, because electrified powertrains provide maximal power across a wide operating range, and single-speed simplifies construction and reduces capital costs. Nevertheless, multi-speed transmissions have also been developed for electric cars, and some of them have recently appeared as commercial products. This paper aims to compare, through some practical examples, solutions with single-speed and dual-speed transmissions. In particular, given the very smooth driving of electric cars, for dual-speed solutions, a dual-clutch gearbox was considered. Finally, a continuously variable transmission (CVT) was also used. Different solutions were analyzed from a technical–economic point of view, based on a simulation of the vehicle under standardized driving cycles, thus evaluating the capital and running electricity costs. The obtained results show that the comparison between the two solutions is very open, and in the majority of cases, the advantages in terms of efficiency overcome the disadvantages due to the additional capital costs. For a rather low battery cost of 150 €/kWh, the total cost reduction moves from about 100–150 € up to 1500–2000 €, depending on the electricity cost, along the whole vehicle lifespan.","PeriodicalId":509694,"journal":{"name":"Vehicles","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139159109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities 电动汽车应用中先进电池管理系统的人工智能方法：面向未来研究机会的统计分析

Vehicles Pub Date : 2023-12-25 DOI: 10.3390/vehicles6010002

M. S. H. Lipu, Md. Sazal Miah, T. Jamal, Tuhibur Rahman, Shaheer Ansari, Md. Siddikur Rahman, R. H. Ashique, ASM Shihavuddin, Mohammed Nazmus Shakib

{"title":"Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities","authors":"M. S. H. Lipu, Md. Sazal Miah, T. Jamal, Tuhibur Rahman, Shaheer Ansari, Md. Siddikur Rahman, R. H. Ashique, ASM Shihavuddin, Mohammed Nazmus Shakib","doi":"10.3390/vehicles6010002","DOIUrl":"https://doi.org/10.3390/vehicles6010002","url":null,"abstract":"In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs.","PeriodicalId":509694,"journal":{"name":"Vehicles","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139158290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine-Learning-Based Design Optimization of Chassis Bushings 基于机器学习的底盘衬套设计优化

Vehicles Pub Date : 2023-12-23 DOI: 10.3390/vehicles6010001

E. Töpel, Alexander Fuchs, K. Büttner, Michael Kaliske, G. Prokop

{"title":"Machine-Learning-Based Design Optimization of Chassis Bushings","authors":"E. Töpel, Alexander Fuchs, K. Büttner, Michael Kaliske, G. Prokop","doi":"10.3390/vehicles6010001","DOIUrl":"https://doi.org/10.3390/vehicles6010001","url":null,"abstract":"In this work, a method is developed for the component design of chassis bushings with contoured inner cores, aided by artificial neural networks (ANNs) and design optimization. First, a model of a physical chassis bushing is generated using the finite element method (FEM). To determine the material parameters of the material model, a material parameter optimization is conducted. Based on the bushing model, different samples for a design study are generated using the design of experiments method. Due to invalid areas of the geometrical model definitions, constraints are established and the design parameter space is cleaned up. From the cleaned design parameter space, a database of several design parameter samples and three associated quasi-static stiffnesses, calculated with FEM simulations, is generated. The database is subsequently used for the training and hyper-parameter optimization of the ANN. Subsequently, the feed-forward ANN is employed in a design study, where stiffnesses are prescribed and design parameters identified. The design process is inverted with the help of a constrained design parameter optimization (DO), based on particle swarm optimization (PSO). Two usecases are defined for the evaluation of the design accuracy of the entire method. The design parameters found are validated by corresponding FEM simulations.","PeriodicalId":509694,"journal":{"name":"Vehicles","volume":"20 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139162229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Driving Standardization in Infrastructure Monitoring: A Role for Connected Vehicles 推动基础设施监测标准化：车联网的作用

Vehicles Pub Date : 2023-12-18 DOI: 10.3390/vehicles5040101

R. Bridgelall

{"title":"Driving Standardization in Infrastructure Monitoring: A Role for Connected Vehicles","authors":"R. Bridgelall","doi":"10.3390/vehicles5040101","DOIUrl":"https://doi.org/10.3390/vehicles5040101","url":null,"abstract":"This study tackles the urgent need for efficient condition monitoring of road and rail infrastructure, which is integral to a nation’s economic vitality. Traditional methods proved both costly and inadequate, resulting in network gaps and accelerated infrastructure decay. Employing connected vehicles with integrated sensors and cloud computing capabilities can provide a cost-effective, sustainable solution for comprehensive infrastructure monitoring. In advocating for international standardization, this study furnishes compelling evidence—encompassing trends in transportation, economics, and patent landscapes—that underscores the necessity and advantages of such standards. The analysis confirmed that trucks and rail will remain dominant in freight transport as infrastructure limitations intensify. A noteworthy finding is the absence of patented solutions in this domain, which simplifies the path toward global standardization. By integrating data from diverse sources, agencies can optimize maintenance triggers and allocate funds more strategically, thus preserving vital transportation networks. These insights not only offer an effective alternative to current practices but also have the potential to influence policymaking and industry standards for infrastructure monitoring.","PeriodicalId":509694,"journal":{"name":"Vehicles","volume":"29 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139174118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental Study to Increase the Autonomy of a UAV by Incorporating Solar Cells 利用太阳能电池提高无人飞行器自主性的实验研究

Vehicles Pub Date : 2023-12-17 DOI: 10.3390/vehicles5040100

João Pedro Sampaio Saloio, G. Cruz, Vasco Coelho, J. P. N. Torres, Ricardo A. Marques Lameirinhas

引用次数: 0