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Vehicles
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Novel Solutions for Transportation Safety, 2nd Edition
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Novel Solutions for Transportation Safety, 2nd Edition

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2997

Special Issue Editors

Dr. Tai-Jin Song

E-Mail Website
Guest Editor
Department of Urban Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea
Interests: traffic safety; big data analytics; digital infrastructure for traffic accident investigation
Special Issues, Collections and Topics in MDPI journals
Dr. Yao Cheng

E-Mail Website
Guest Editor
Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
Interests: arterial safety; traffic operation; signal optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Transportation remains the backbone of economic growth and societal connectivity, and ensuring safety is more critical than ever. Building on the success of the first edition, this second edition of our Special Issue continues to explore cutting-edge research and innovative strategies for enhancing transportation safety, while introducing new and emerging topics that reflect the evolution of the field. In addition to core themes such as accident prevention, traffic incident management, smart infrastructure for traffic safety, and autonomous vehicle systems, this edition expands its scope to include the following topics: (1) preventive safety project development; (2) the adoption of machine learning (ML) in safety applications; and (3) vulnerable road user (VRU) safety.

We invite contributions that address these topics and other forward-thinking approaches to transportation safety. By fostering dialogue on these evolving areas, this Special Issue aims to shape safer, more reliable transportation networks for communities worldwide.

Dr. Tai-Jin Song
Dr. Yao Cheng
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Vehicles 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 1800 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 safety
  • autonomous vehicle
  • data-driven approach
  • incident management

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Related Special Issue

Published Papers (2 papers)

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Research

23 pages, 2962 KB  
Article
Feasibility of Infrared-Based Pedestrian Detectability in Unlit Urban and Rural Road Sections Using Consumer Thermal Cameras
by Yordan Stoyanov, Atanasi Tashev and Penko Mitev
Vehicles 2026, 8(3), 61; https://doi.org/10.3390/vehicles8030061 - 16 Mar 2026
Viewed by 332
Abstract
This study evaluates the feasibility of using two affordable thermal cameras (UNI-T UTi260M and UTi260T), which are not designed as automotive sensors, for observing pedestrians and warm objects during night-time driving under low-illumination conditions. The experimental setup includes mounting the camera on the [...] Read more.
This study evaluates the feasibility of using two affordable thermal cameras (UNI-T UTi260M and UTi260T), which are not designed as automotive sensors, for observing pedestrians and warm objects during night-time driving under low-illumination conditions. The experimental setup includes mounting the camera on the vehicle body (e.g., side mirror area/roof), recording road scenes in urban and rural environments, and selecting representative frames for qualitative and quantitative analysis. The study assesses: (i) observable pedestrian detectability in unlit road sections and under oncoming headlight glare, where visible cameras often lose contrast; (ii) the influence of low ambient temperature and strong cold wind on image appearance (including “whitening”/contrast shifts); and (iii) workflow differences, where UTi260M relies on a smartphone application for streaming/recording, while UTi260T supports PC-based image analysis and temperature-profile visualization. In addition, a calibration-based geometric method is proposed for approximate pedestrian distance estimation from single frames using silhouette pixel height and a regression model based on 1/hpx, valid for a specific mounting configuration and a known subject height. Results indicate that both cameras can highlight warm objects relative to the background and support visual pedestrian identification at low illumination, including in the presence of oncoming headlights, with UTi260M showing more stable behavior in parts of the tests. This work is a feasibility study and does not claim Advanced Driver Assist Systems (ADAS) functionality; it outlines limitations, repeatability considerations, and a minimal set of metrics and procedures for future extension. All quantitative indicators derived from exported frames are explicitly treated as image-level proxy metrics, not as physical sensor characteristics. Full article
(This article belongs to the Special Issue Novel Solutions for Transportation Safety, 2nd Edition)
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17 pages, 1932 KB  
Article
Advanced Multi-Modal Sensor Fusion System for Detecting Falling Humans: Quantitative Evaluation for Enhanced Vehicle Safety
by Nick Barua and Masahito Hitosugi
Vehicles 2025, 7(4), 149; https://doi.org/10.3390/vehicles7040149 - 1 Dec 2025
Viewed by 2301
Abstract
Collisions with fallen pedestrians pose a lethal challenge to current advanced driver-assistance systems. This paper introduces and quantitatively validates the Advanced Falling Object Detection System (AFODS), a novel safety framework designed to mitigate this risk. AFODS architecturally integrates long-wave infrared, near-infrared stereo and [...] Read more.
Collisions with fallen pedestrians pose a lethal challenge to current advanced driver-assistance systems. This paper introduces and quantitatively validates the Advanced Falling Object Detection System (AFODS), a novel safety framework designed to mitigate this risk. AFODS architecturally integrates long-wave infrared, near-infrared stereo and ultrasonic sensors, processed through a novel artificial intelligence pipeline that combines YOLOv7-Tiny for object detection with a recurrent neural network for proactive threat assessment, thereby enabling the system to predict falls before they are complete. In a rigorous controlled study using simulated adverse conditions, AFODS achieved a 98.2% detection rate at night, a condition where standard systems fail. This paper details the system’s ISO 26262-aligned architecture and validation results, proposing a framework for a new benchmark in active vehicle safety, demonstrated under controlled test conditions. Full article
(This article belongs to the Special Issue Novel Solutions for Transportation Safety, 2nd Edition)
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