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Sustainable Mobility and Transportation (SMTS 2025)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 177

Special Issue Editors


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Guest Editor
Audi Hungaria Faculty of Vehicle Engineering, Széchenyi István University, Egyetem tér 1, Győr H-9026, Hungary
Interests: polymer composites; injection molding technologies; material testing

E-Mail Website
Guest Editor
Department at Road and Rail Vehicles, Széchenyi István University, Győr 9026, Hungary
Interests: sustainable transportation; energy efficiency; electric vehicles; battery testing; battery diagnostics

Special Issue Information

Dear Colleagues,

This Special Issue is compiled in cooperation with the 2nd Sustainable Mobility and Transportation Symposium, an interdisciplinary scientific conference organized by the Audi Hungaria Faculty of Vehicle Engineering and the Vehicle Industry Research Center of Széchenyi István University. The symposium is scheduled to take place from 16–18 October 2025 in Győr, Hungary. Following the success of last year’s event, the 2025 symposium continues to focus on the latest advancements, challenges, and innovations in automotive technology and transportation systems. The conference provides a platform for researchers, scholars, professionals, and enthusiasts from diverse fields to collaborate, share knowledge, and envision the future of sustainable mobility. We welcome submissions from participants of the symposium, with the aim being to publish selected papers in this Special Issue of Applied Sciences, thereby further disseminating high-quality research contributions from the event.

Prof. Dr. Dogossy Gábor
Dr. Szabolcs Kocsis Szürke
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • vehicle development
  • diagnostics and testing
  • materials technology
  • autonomous vehicle
  • fuels and lubricants
  • electromobility
  • mobility and society
  • AI and control
  • manufacturing technology

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Published Papers (1 paper)

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Research

18 pages, 1079 KiB  
Article
Driver Clustering Based on Individual Curve Path Selection Preference
by Gergo Igneczi, Tamas Dobay, Erno Horvath and Krisztian Nyilas
Appl. Sci. 2025, 15(14), 7718; https://doi.org/10.3390/app15147718 (registering DOI) - 9 Jul 2025
Abstract
The development of Advanced Driver Assistance Systems (ADASs) has reached a stage where, in addition to the traditional challenges of path planning and control, there is an increasing focus on the behavior of these systems. Assistance functions shall be personalized to deliver a [...] Read more.
The development of Advanced Driver Assistance Systems (ADASs) has reached a stage where, in addition to the traditional challenges of path planning and control, there is an increasing focus on the behavior of these systems. Assistance functions shall be personalized to deliver a full user experience. Therefore, driver modeling is a key area of research for next-generation ADASs. One of the most common tasks in everyday driving is lane keeping. Drivers are assisted by lane-keeping systems to keep their vehicle in the center of the lane. However, human drivers often deviate from the center line. It has been shown that the driver’s choice to deviate from the center line can be modeled by a linear combination of preview curvature information. This model is called the Linear Driver Model. In this paper, we fit the LDM parameters to real driving data. The drivers are then clustered based on the individual parameters. It is shown that clusters are not only formed by the numerical similarity of the driver parameters, but the drivers in a cluster actually have similar behavior in terms of path selection. Finally, an Extended Kalman Filter (EKF) is proposed to learn the model parameters at run-time. Any new driver can be classified into one of the driver type groups. This information can be used to modify the behavior of the lane-keeping system to mimic human driving, resulting in a more personalized driving experience. Full article
(This article belongs to the Special Issue Sustainable Mobility and Transportation (SMTS 2025))
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