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Future Transportation
Special Issues
Future of Vehicles (FoV2026)
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Future of Vehicles (FoV2026)

A Special Issue of Future Transportation (ISSN 2673-7590).

Deadline for manuscript submissions: 31 January 2027 | Viewed by 725

Editors

Prof. Dr. István Lakatos

E-Mail Website
Guest Editor
Department of Road and Rail Vehicles, Széchenyi István University, H-9026 Győr, Hungary
Interests: vehicle engine diagnostics; chassis dynamometer performance testing; combustion engine cold-start and idling behavior; alternative fuels for diesel engines; electric and hybrid vehicle diagnostics; vehicle online diagnostics and predictive maintenance; vehicle dynamics modeling and simulations; Vehicle safety and acoustic diagnostics; seat safety in non-conventional seating positions; sensor calibration and ADAS sensing systems
Special Issues, Collections and Topics in MDPI journals
Dr. Zoltán Weltsch

E-Mail Website
Guest Editor
Zalaegerszeg Innovation Park, Széchenyi István University, H-8900 Zalaegerszeg, Hungary
Interests: mechanical engineering; vehicle engineering; vehicle dynamics; autonomous vehicles; advanced driver-assistance system (ADAS); vehicle testing and validation; transportation safety; electric and hybrid vehicles; sustainable transportation systems; powertrain development; vehicle-to-everything (V2X) communication; simulation and modelling in vehicle engineering; hydrogen–gasoline dual-fuel internal combustion engines; hydrogen combustion modeling and optimization; dual-fuel engine efficiency and emissions; combustion stability and knock analysis in hydrogen blends
Special Issues, Collections and Topics in MDPI journals
Dr. Leticia Pekk

E-Mail Website
Guest Editor
Zalaegerszeg Innovation Park, Széchenyi István University, H-8900 Zalaegerszeg, Hungary
Interests: technology forecasting; technology management; technological competence management; corporate R&D strategies; innovation management; sustainable transportation systems; automotive testing and validation; advanced driver-assistance system (ADAS); vehicle communication networks (CAN, V2X); electric vehicle technologies; battery diagnostics and energy management; smart city mobility solutions; agrivoltaics and renewable integration in transportation; industrial sustainability models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The automotive and mobility sectors are experiencing one of the most significant transformations in their history. Electrification, connected and autonomous systems, artificial intelligence, advanced manufacturing technologies, and sustainable transportation solutions are reshaping how vehicles are designed, produced, and operated. At the same time, increasing environmental expectations, evolving regulatory frameworks, and global economic challenges require innovative and interdisciplinary approaches to future mobility.

The Future of Vehicles Conference 2026 (FoV2026), to be held in Zalaegerszeg, Hungary, aims to provide an international forum for researchers, industry professionals, policymakers, and technology experts to discuss the latest developments and challenges in vehicle engineering, intelligent transportation systems, and mobility innovation. The conference promotes collaboration between academia and industry while supporting the dissemination of cutting-edge scientific and technological achievements.

FoV2026 features distinguished keynote speakers from internationally recognized universities, research institutions, and industrial organizations, representing diverse fields of expertise related to future mobility, vehicle technologies, digitalization, and sustainable transportation. Their contributions will provide valuable insights into emerging trends, technological challenges, and future research directions.

This Special Issue is dedicated to publishing high-quality contributions presented at FoV2026, while also welcoming submissions from the broader international research community. The objective is to advance scientific knowledge and practical solutions that contribute to safer, smarter, more sustainable, and economically viable transportation systems.

By bringing together innovative research from multiple disciplines, this Special Issue seeks to strengthen the connection between theoretical advancements and industrial implementation. It also aims to foster collaboration across engineering, computer science, economics, management, and policy domains, recognizing that the future of mobility requires integrated and multidisciplinary solutions.

Topics of interest include, but are not limited to, the following:

  • Electric, hybrid, hydrogen-powered, and alternative-fuel vehicle technologies;
  • Autonomous vehicles, automated driving systems, and advanced driver-assistance systems (ADASs);
  • Artificial intelligence, machine learning, and data-driven mobility solutions;
  • Vehicle-to-everything (V2X) communication and connected transportation systems;
  • Smart infrastructure and intelligent transport systems;
  • Vehicle simulation, testing, validation, and digital twin applications;
  • Sustainable manufacturing technologies and lightweight materials;
  • Energy management, battery technologies, and renewable energy integration;
  • Cybersecurity, safety, and reliability of modern transportation systems;
  • Traffic management, mobility optimization, and smart city solutions;
  • Technology forecasting, innovation management, and strategic decision-making in mobility;
  • Economic, regulatory, and policy aspects of transportation transformation;
  • Emerging technologies and future trends in automotive and mobility systems.

We warmly invite researchers, engineers, industry experts, and policymakers to contribute their latest findings and perspectives. We look forward to receiving your submissions and to advancing the scientific dialogue that will help shape the future of vehicles and mobility systems.

Prof. Dr. István Lakatos
Dr. Zoltán Weltsch
Dr. Leticia Pekk
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Transportation 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 1200 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

  • future of vehicles
  • sustainable mobility
  • autonomous driving
  • connected vehicles
  • vehicle-to-everything
  • artificial intelligence
  • advanced driver-assistance systems
  • electric vehicles
  • alternative powertrains
  • smart infrastructure
  • intelligent transportation systems
  • technology management

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

22 pages, 311 KB  
Article
GPU-Accelerated Guided Heuristic Sampling for Residual Error Probability Analysis in CAN FD Communication
by Krisztián Koller and Balázs Baráth
Future Transp. 2026, 6(5), 207; https://doi.org/10.3390/futuretransp6050207 - 24 Sep 2026
Abstract
The deployment of automated driving features demands stringent compliance with ISO 26262, particularly concerning data integrity over in-vehicle networks. This study effectively addresses the computational challenges of quantifying the residual error probability of Classical CAN and CAN FD communication under high-order fault profiles. [...] Read more.
The deployment of automated driving features demands stringent compliance with ISO 26262, particularly concerning data integrity over in-vehicle networks. This study effectively addresses the computational challenges of quantifying the residual error probability of Classical CAN and CAN FD communication under high-order fault profiles. By introducing a GPU-accelerated residual error analysis framework utilizing OpenCL, it overcomes the mathematical barriers of traditional brute-force simulation, elevating execution speeds from 150,000 to 4.2 million iterations per second. Empirical evaluations demonstrate that native data-link layer CRC protection may be bypassed under specific multi-bit physical-layer corruption patterns involving stuff-bit cascading effects. Conversely, for the evaluated application-layer End-to-End (E2E) protected configurations, no residual errors were observed. However, from a functional safety management perspective, these observations must be interpreted within the scope of the investigated configurations: the localized payload regions between Bytes 12 and 16 exhibiting an elevated susceptibility to masking failures apply exclusively to the investigated radar payload structures and selected real-world BLF traces. Because these masking failures are highly dependent on the evaluated frame structures and specific fault-injection scenarios, the conclusions should be limited accordingly to the evaluated configurations. Full article
(This article belongs to the Special Issue Future of Vehicles (FoV2026))
13 pages, 1212 KB  
Article
Priority Flicker in Risk-Based Pedestrian Prioritization: A Baseline Temporal Stability Assessment on the ETH/UCY Benchmark
by Zoltán Rózsás and István Lakatos
Future Transp. 2026, 6(5), 189; https://doi.org/10.3390/futuretransp6050189 - 7 Sep 2026
Viewed by 158
Abstract
Risk-based prioritization frameworks such as the Intelligent Pedestrian Model (IPM) rank pedestrians by an instantaneous, reference-normalized risk score. They indicate which pedestrian requires attention first. This study examines the temporal stability of such rankings. We computed an observation-only kinematic Exposure proxy frame by [...] Read more.
Risk-based prioritization frameworks such as the Intelligent Pedestrian Model (IPM) rank pedestrians by an instantaneous, reference-normalized risk score. They indicate which pedestrian requires attention first. This study examines the temporal stability of such rankings. We computed an observation-only kinematic Exposure proxy frame by frame on three ETH/UCY benchmark scenes. In these scenes, the highest-priority identity changes rapidly: the median top-1 persistence is two frames (0.8 s). We introduce a switch classification that separates established switches from entry-driven and forced switches. Grace-period exclusion is evaluated as a sensitivity variant and shown to remove up to 82% of evaluable time in short-track scenes. Established flicker rates range from one switch per 2.7 s in dense scenes to one per 25.4 s in sparse scenes, with established switches concentrated at small score differences between competing pedestrians. The results show that instantaneous rankings alone may be insufficient for sustained attention allocation and motivate future work on temporal priority management. Full article
(This article belongs to the Special Issue Future of Vehicles (FoV2026))
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22 pages, 3691 KB  
Article
Isotropic Coordinate Normalization and Target-Aware Search for Vehicle Trajectory Clustering at Complex Urban Intersections
by Áron Dávid Agg and András Horváth
Future Transp. 2026, 6(5), 181; https://doi.org/10.3390/futuretransp6050181 - 25 Aug 2026
Viewed by 209
Abstract
Grouping vehicles with similar paths is important for traffic analysis, but camera-image trajectories are distorted by perspective, and unsupervised clustering does not directly reveal how many traffic movements should be expected. This paper presents Homography-Guided Semantic Maneuver Graph Trajectory Clustering (HG-SMG-TC), a model-selection [...] Read more.
Grouping vehicles with similar paths is important for traffic analysis, but camera-image trajectories are distorted by perspective, and unsupervised clustering does not directly reveal how many traffic movements should be expected. This paper presents Homography-Guided Semantic Maneuver Graph Trajectory Clustering (HG-SMG-TC), a model-selection framework that uses a lightweight homography to estimate intersection structure while retaining isotropically normalized camera coordinates for clustering. Entry and exit endpoint groups are consolidated into physical approach-level groups, and their supported origin–destination relationships form a maneuver graph. Bootstrap resampling converts this structure into an interval for the expected number of observed movements, which guides clustering model selection. The method is evaluated on 67,029 vehicle trajectories from five urban intersection scenes in the Traffic Node Video Dataset, using separate target-estimation, model-selection, and independent-test recording blocks. Independent polygon-rule reference labels cover 89.6–98.4% of test trajectories. HG-SMG-TC reduces mean target-count error from 3.20 for untargeted selection and 2.53 for the point-target variant to 2.13, while achieving an adjusted Rand index of 0.734 and normalized mutual information of 0.839. The results show that the proposed semantic maneuver prior improves target alignment and provides a reproducible way to guide unsupervised trajectory clustering, while retaining explicit trade-offs across evaluation metrics. Full article
(This article belongs to the Special Issue Future of Vehicles (FoV2026))
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