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Machines
Special Issues
Modeling, Estimation, Control, and Decision for Human-Vehicle Systems
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Modeling, Estimation, Control, and Decision for Human-Vehicle Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 4546

Special Issue Editors

Prof. Dr. Xuewu Ji

E-Mail Website
Guest Editor
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Interests: human–vehicle cooperative steering control; intelligent vehicle motion control
Dr. Jinxiang Wang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Interests: vehicle dynamics and control; assisted-driving system; control of autonomous vehicles
Prof. Dr. Kimihiko Nakano

E-Mail Website
Guest Editor
Advanced Mobility Research Center, The University of Tokyo, 4 -6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
Interests: vehicle dynamics and control; automated driving; human factor
Prof. Dr. Jian Wu

E-Mail Website
Guest Editor
School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China
Interests: vehicle dynamics and chassis dynamic coordination control; automated vehicles and intelligent control; human–machine collaborative control
Special Issues, Collections and Topics in MDPI journals
Dr. Zheng Wang

E-Mail Website
Guest Editor
Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
Interests: driver assistance systems; human system interaction; intelligent transport systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the rapid development of artificial intelligence has created opportunities for revolutionary changes to the automotive industry. Autonomous driving technology, integrating environmental perception, planning and decision making, and advanced driving assistance systems, is gradually being widely used in intelligent vehicles. However, limited by technical bottlenecks, laws, ethics, etc., human drivers, as the subject of decision-making control, will be retained in intelligent vehicle systems in the longer term. Since drivers play a critical role in the transportation system, humans and machines will share decision making and control over the intelligent vehicles.

In practice, drivers with different skills and behaviors exhibit different driving characteristics. The driver–automation interaction is an interconnected and complicated system. Driver behavior analysis and modeling, as well as personalized decision making and control, are key issues in human–vehicle systems. Hence, this Special Issue aims to provide collect research concepts, theoretical findings and practical solutions to modeling, estimation, control, and decision-making issues in human–vehicle systems. This will assist in the interaction between human driver and vehicle automation.

Prof. Dr. Xuewu Ji
Dr. Jinxiang Wang
Prof. Dr. Kimihiko Nakano
Prof. Dr. Jian Wu
Dr. Zheng Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • advanced driver assistance systems
  • driver–automation shared control
  • driver cognitive behaviors
  • personalized decision and control for autonomous driving

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Published Papers (2 papers)

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18 pages, 11369 KiB  
Article
Analysis of E-Scooter Vibrations from Health Perspective: A Case Study
by Juan David Cano-Moreno, José María Cabanellas Becerra, José Manuel Arenas Reina and Manuel Enrique Islán Marcos
Machines 2023, 11(7), 761; https://doi.org/10.3390/machines11070761 - 21 Jul 2023
Cited by 2 | Viewed by 2176
Abstract
The impact of vibrations on health in occupational environments has been extensively studied. Although the effects of vehicle vibrations on driving comfort has been investigated, the literature on the impact of vehicle vibrations on health is scarce. Accordingly, this study aimed to investigate [...] Read more.
The impact of vibrations on health in occupational environments has been extensively studied. Although the effects of vehicle vibrations on driving comfort has been investigated, the literature on the impact of vehicle vibrations on health is scarce. Accordingly, this study aimed to investigate the influence of e-scooter vibrations on driver health by considering both whole-body vibrations (WBVs) and hand–arm vibrations (HAVs). From the perspective of health, vibration zones were defined based on the UNE-2631 and UNE-5349 standards, as well as the European Vibration Directive. Real measurements obtained from an e-scooter acceleration database were used. The results of the study on WBVs show that, on average, 87.54% and 95.47% of non-desirable vibrations are caused by driving an e-scooter on pavers and asphalt, respectively. This shows that ‘potentially non-healthy’ and ‘non-healthy’ vibrations are 25.69% and 61.85%, respectively, when driving on pavers and 85.52% and 12.96%, respectively, when driving on asphalt. Therefore, the WBV levels reached by driving an e-scooter on any pavement could potentially harm health. However, the influence of HAV on the incidence of Raynaud’s syndrome is low. The study results on WBV suggest that future e-scooter designs must be based on a more damped road–driver interface. Full article
(This article belongs to the Special Issue Modeling, Estimation, Control, and Decision for Human-Vehicle Systems)
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22 pages, 5296 KiB  
Article
An Evaluation Method for Automated Vehicles Combining Subjective and Objective Factors
by Wei Wang, Liguang Wu, Xin Li, Fufan Qu, Wenbo Li, Yangyang Ma and Denghui Ma
Machines 2023, 11(6), 597; https://doi.org/10.3390/machines11060597 - 1 Jun 2023
Cited by 3 | Viewed by 1943
Abstract
The rapid development of automated vehicle technology requires reasonable test scenarios and comprehensive evaluation methods. This paper proposes an evaluation method for automated vehicles combining subjective and objective factors. First, we propose a method for automatically generating test scenarios and for batch testing [...] Read more.
The rapid development of automated vehicle technology requires reasonable test scenarios and comprehensive evaluation methods. This paper proposes an evaluation method for automated vehicles combining subjective and objective factors. First, we propose a method for automatically generating test scenarios and for batch testing autonomous vehicles. Then, the use of the target layer, total index layer, and index layer of automated vehicles is proposed to establish a more comprehensive evaluation system for automated vehicles. Specifically, the analytic hierarchy process (AHP, subjective) and improved criteria importance though intercriteria correlation (CRITIC, objective) methods are used to determine the weight of the indicators, and a two-level fuzzy comprehensive (subjective and objective) evaluation method is adopted to comprehensively evaluate the performance of the automated vehicles. Finally, the effectiveness of the proposed evaluation method combining subjective and objective factors is verified through virtual simulations and real-world experiments. Through a combination of subjective and objective methods, improved results can be obtained for safety, efficiency, economy, intelligence, and comfort tests. Full article
(This article belongs to the Special Issue Modeling, Estimation, Control, and Decision for Human-Vehicle Systems)
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