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Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety...
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Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety in Transportation

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 6965

Special Issue Editors

Dr. Eva Michelaraki

E-Mail Website
Guest Editor
Department of Transportation Planning and Engineering, National Technical University of Athens, 15773 Athens, Greece
Interests: traffic engineering; driver behaviour; road safety; crash analysis; statistical modelling; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. George Yannis

E-Mail Website
Guest Editor
Department of Transportation Planning and Engineering, National Technical University of Athens, 15773 Athens, Greece
Interests: road safety; transportation planning and management; urban mobility; intelligent transportation systems; automation; data management and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of transportation technologies and the integration of Artificial Intelligence (AI) are reshaping the future of road safety and smart mobility. As the complexity of traffic systems increases, understanding driver behaviour, enhancing vehicle safety, and implementing intelligent transportation solutions have become critical for reducing road crashes and improving overall mobility. Emerging innovations, including AI-driven decision-making, advanced driver-assistance systems (ADAS), vehicle-to-everything (V2X) communication, and smart infrastructure, are paving the way for safer and more efficient transportation networks.

This Special Issue, “Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety in Transportation”, aims to present cutting-edge research on novel solutions and technological advancements in smart mobility, transportation safety, and intelligent vehicle systems. We welcome contributions that explore data-driven approaches to traffic management, machine learning applications for driver behaviour analysis, crash prediction and prevention strategies, automation in transportation systems, and the role of connected and autonomous vehicles (CAVs) in improving road safety.

We invite original contributions covering, but not limited to, the following topics:

  • AI-driven innovations in road safety and driver behaviour analysis.
  • Advanced vehicle safety systems and state-of-the-art monitoring technologies.
  • Intelligent transportation systems (ITS) and adaptive traffic management.
  • Human factors and decision-making in automated driving.
  • Emerging vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) safety applications.
  • Smart mobility solutions and their impact on urban and highway safety.
  • The role of AI, big data, and predictive analytics in crash prevention.
  • Future mobility trends and advanced digital tools.

Dr. Eva Michelaraki
Prof. Dr. George Yannis
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 quarterly 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 1600 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

  • road safety
  • travel behaviour
  • smart mobility
  • transportation systems
  • cooperative, connected and automated mobility
  • intelligent connected vehicles
  • vehicular communication technologies
  • autonomous vehicles

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

Published Papers (7 papers)

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Research

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14 pages, 492 KB  
Article
Analysis of the Willingness to Shift to Electric Vehicles: Critical Factors and Perspectives
by Antonio Comi, Umberto Crisalli, Olesia Hriekova and Ippolita Idone
Vehicles 2025, 7(4), 159; https://doi.org/10.3390/vehicles7040159 - 10 Dec 2025
Abstract
Urbanisation and the increasing concentration of populations in cities present significant challenges for achieving sustainable mobility and advancing the energy transition. Private vehicles, particularly those powered by internal combustion engines, remain the primary contributors to urban air pollution and greenhouse gas emissions. This [...] Read more.
Urbanisation and the increasing concentration of populations in cities present significant challenges for achieving sustainable mobility and advancing the energy transition. Private vehicles, particularly those powered by internal combustion engines, remain the primary contributors to urban air pollution and greenhouse gas emissions. This situation has prompted the European Union to accelerate transport decarbonisation through comprehensive policy frameworks, notably the “Fit for 55” package, which aims to reduce net greenhouse gas emissions by 55% by 2030. These measures underscore the urgency of shifting towards low-emission transport modes. In this context, electric vehicles (EVs) play a key role in supporting Sustainable Development Goal 7 by promoting cleaner and more efficient transport solutions, and Sustainable Development Goal 11, aimed at creating more sustainable and liveable cities. Despite growing policy attention, the adoption of EVs remains constrained by users’ concerns regarding purchase costs, driving range, and the availability of charging infrastructure, as shown by the findings of this study. In this context, this study explores the determinants of EV adoption in Italy by employing a combined methodological approach that integrates a stated preference (SP) survey with discrete choice modelling. The analysis aims to quantify the influence of economic, technical, and infrastructural factors on users’ willingness to switch to EVs, providing insights for policymakers and industry stakeholders to design effective strategies for accelerating the transition toward the sustainable mobility. Full article
(This article belongs to the Special Issue Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety in Transportation)
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38 pages, 4380 KB  
Article
Enhancement of ADAS with Driver-Specific Gaze Profiling Algorithm—Pilot Case Study
by Marián Gogola and Ján Ondruš
Vehicles 2025, 7(4), 145; https://doi.org/10.3390/vehicles7040145 - 28 Nov 2025
Viewed by 150
Abstract
This study investigates drivers’ visual attention strategies during naturalistic urban driving using mobile eye-tracking (Pupil Labs Neon). A sample of experienced drivers participated in a realistic traffic scenario to examine fixation behaviour under varying traffic conditions. Non-parametric analyses revealed substantial variability in fixation [...] Read more.
This study investigates drivers’ visual attention strategies during naturalistic urban driving using mobile eye-tracking (Pupil Labs Neon). A sample of experienced drivers participated in a realistic traffic scenario to examine fixation behaviour under varying traffic conditions. Non-parametric analyses revealed substantial variability in fixation behaviour attributable to driver identity (H(9) = 286.06, p = 2.35 × 10−56), stimulus relevance (H(7) = 182.64, p = 5.40 × 10−36), and traffic density (H(4) = 76.49, p = 9.64 × 10−16). Vehicles and pedestrians elicited significantly longer fixations than lower-salience categories, reflecting adaptive allocation of visual attention to behaviourally critical elements of the scene. Compared with the fixed-rule method, which produced inflated anomaly rates of 7.23–14.84% (mean 12.06 ± 2.71%), the DSGP algorithm yielded substantially lower and more stable rates of 1.62–3.33% (mean 2.48 ± 0.53%). The fixed-rule approach over-classified anomalies by approximately 4–6×, whereas DSGP more accurately distinguished contextually appropriate fixations from genuine attentional deviations. These findings demonstrate that fixation behaviour in driving is strongly shaped by individual traits and environmental context, and that driver-specific modelling substantially improves the reliability of attention monitoring. Therefore DSGP framework offers a robust, personalised alternative evaluated at the proof-of-concept level to fixed thresholds and represents a promising direction for enhancing driver-state assessment in future ADAS. Full article
(This article belongs to the Special Issue Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety in Transportation)
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15 pages, 4821 KB  
Article
AI Meets ADAS: Intelligent Pothole Detection for Safer AV Navigation
by Ibrahim Almasri, Dmitry Manasreh and Munir D. Nazzal
Vehicles 2025, 7(4), 109; https://doi.org/10.3390/vehicles7040109 - 28 Sep 2025
Viewed by 1603
Abstract
Potholes threaten public safety and automated vehicles (AVs) safe navigation by increasing accident risks and maintenance costs. Traditional pavement inspection methods, which rely on human assessment, are inefficient for rapid pothole detection and reporting due to potholes’ random and sudden occurring. Advancements in [...] Read more.
Potholes threaten public safety and automated vehicles (AVs) safe navigation by increasing accident risks and maintenance costs. Traditional pavement inspection methods, which rely on human assessment, are inefficient for rapid pothole detection and reporting due to potholes’ random and sudden occurring. Advancements in Artificial Intelligence (AI) now enable automated pothole detection using image-based object recognition, providing innovative solutions to enhance road safety and assist agencies in prioritizing maintenance. This paper proposes a novel approach that evaluates the integration of 3 state-of-the-art AI models (YOLOv8n, YOLOv11n, and YOLOv12n) with an ADAS-like camera, GNSS receiver, and Robot Operating System (ROS) to detect potholes in uncontrolled real-life scenarios, including different weather/lighting conditions and different route types, and generate ready-to-use data in a real-time manner. Tested on real-world road data, the algorithm achieved an average precision of 84% and 84% in recall, demonstrating its effectiveness, stable, and high performance for real-life applications. The results highlight its potential to improve road safety, allow vehicles to detect potholes through ADAS, support infrastructure maintenance, and optimize resource allocation. Full article
(This article belongs to the Special Issue Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety in Transportation)
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24 pages, 1972 KB  
Article
The Impact of Time Delays in Traffic Information Transmission Using ITS and C-ITS Systems: A Case-Study on a Motorway Section Between Two Tunnels
by Iva Meglič, Matjaž Šraml, Ulrich Zorin and Chiara Gruden
Vehicles 2025, 7(4), 107; https://doi.org/10.3390/vehicles7040107 - 25 Sep 2025
Viewed by 818
Abstract
Timely and accurate traffic information is crucial for maintaining safety and efficiency on motorway networks. This research examines time delays in traffic information transmission through intelligent transport systems (ITSs) and cooperative intelligent transport systems (C-ITSs) on the Slovenian motorway network. The aim of [...] Read more.
Timely and accurate traffic information is crucial for maintaining safety and efficiency on motorway networks. This research examines time delays in traffic information transmission through intelligent transport systems (ITSs) and cooperative intelligent transport systems (C-ITSs) on the Slovenian motorway network. The aim of the research is to assess the effectiveness of existing notification systems and the impact of time delays on the timely informing of drivers in the event of an accident in a tunnel. Using real-world data from Regional Traffic Center (RCC) in Vransko, manual and automated activations of traffic portals and different update frequencies of the Promet+ mobile application were analyzed during peak hours. Results show that automated activation reduces delays from 34 to 25 s at portals and from 27 to 18 s in the Promet+ app. Continuous updates in the app provided the highest driver coverage, leaving only 15 uninformed drivers in the morning peak and 8 in the afternoon, whereas 60 s update intervals left up to 71 drivers uninformed. These findings highlight the effectiveness of automation and continuous updates in minimizing delays and improving driver awareness. The research contributes by quantifying latency in ITSs and C-ITSs and demonstrating that their combined use offers the most reliable information delivery. Future improvements should focus on hybrid integration of ITS and C-ITS, dynamic update intervals, and infrastructure upgrades to ensure consistent real-time communication, shorter response times, and enhanced motorway safety. Full article
(This article belongs to the Special Issue Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety in Transportation)
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18 pages, 1724 KB  
Article
Influence of ADAS on Driver Distraction
by Gaetano Bosurgi, Stellario Marra, Orazio Pellegrino, Giuseppe Sollazzo and Alessia Ruggeri
Vehicles 2025, 7(3), 103; https://doi.org/10.3390/vehicles7030103 - 18 Sep 2025
Viewed by 610
Abstract
In recent years, research into smart roads has moved from the purely theoretical phase of initial experiments to an increasing number of applications on new or existing roads. However, a high level of digitization in terms of available equipment may lead to a [...] Read more.
In recent years, research into smart roads has moved from the purely theoretical phase of initial experiments to an increasing number of applications on new or existing roads. However, a high level of digitization in terms of available equipment may lead to a decrease in driving performance and, consequently, have a negative impact on safety. The aim of this study is to define a procedure to determine the impact of these technologies by analyzing the visual behavior of the driver, in order to refine the on-board devices in case of negative feedback. The visual strategy of a sample of users was evaluated during simulated driving. Their behavior, recorded by an eye tracker, showed that the introduction of an On-Board Unit (OBU) makes drivers more aware of the road. In fact, even if the number of fixations towards the OBU increases, the average duration of each fixation decreases and remains below the alarm thresholds indicated in the literature. Full article
(This article belongs to the Special Issue Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety in Transportation)
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28 pages, 898 KB  
Article
ADAS Technologies and User Trust: An Area-Based Study with a Sociodemographic Focus
by Salvatore Leonardi and Natalia Distefano
Vehicles 2025, 7(3), 67; https://doi.org/10.3390/vehicles7030067 - 4 Jul 2025
Cited by 1 | Viewed by 1261
Abstract
This study investigates the knowledge, perception and trust in Advanced Driver Assistance Systems (ADAS) among drivers in Eastern Sicily, a Mediterranean region characterized by infrastructural and socio-economic differences. A structured survey (N = 961) was conducted to assess user attitudes towards eight key [...] Read more.
This study investigates the knowledge, perception and trust in Advanced Driver Assistance Systems (ADAS) among drivers in Eastern Sicily, a Mediterranean region characterized by infrastructural and socio-economic differences. A structured survey (N = 961) was conducted to assess user attitudes towards eight key ADAS technologies using two validated indices: the Knowledge Index (KI) and the Importance Index (II). To capture user consistency, a normalized product (z(KI) × z(II)) was calculated for each technology. This composite metric enabled the identification of three latent dimensions through exploratory factor analysis: Emergency-Triggered Systems, Adaptive and Reactive Systems and Driver Vigilance and Stability Systems. The results show a clear discrepancy between perceived importance (56.6%) and actual knowledge (35.1%). Multivariate analyses show that direct experience with ADAS-equipped vehicles significantly increases both awareness and confidence. Age is inversely correlated with knowledge, while gender has only a marginal influence. The results are consistent with established acceptance models such as TAM and UTAUT, which emphasize the role of perceived usefulness and trust. The study presents an innovative integration of psychometric metrics and behavioral theory that provides a robust and scalable framework for assessing user readiness in evolving mobility contexts, particularly in regions facing infrastructural heterogeneity and cultural changes in travel behavior. Full article
(This article belongs to the Special Issue Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety in Transportation)
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Review

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30 pages, 1709 KB  
Review
Performance of Advanced Rider Assistance Systems in Varying Weather Conditions
by Zia Ullah, João A. C. da Silva, Ricardo Rodrigues Nunes, Arsénio Reis, Vítor Filipe, João Barroso and E. J. Solteiro Pires
Vehicles 2025, 7(4), 105; https://doi.org/10.3390/vehicles7040105 - 24 Sep 2025
Cited by 1 | Viewed by 1586
Abstract
Advanced rider assistance systems (ARAS) play a crucial role in enhancing motorcycle safety through features such as collision avoidance, blind-spot detection, and adaptive cruise control, which rely heavily on sensors like radar, cameras, and LiDAR. However, their performance is often compromised under adverse [...] Read more.
Advanced rider assistance systems (ARAS) play a crucial role in enhancing motorcycle safety through features such as collision avoidance, blind-spot detection, and adaptive cruise control, which rely heavily on sensors like radar, cameras, and LiDAR. However, their performance is often compromised under adverse weather conditions, leading to sensor interference, reduced visibility, and inconsistent reliability. This study evaluates the effectiveness and limitations of ARAS technologies in rain, fog, and snow, focusing on how sensor performance, algorithms, techniques, and dataset suitability influence system reliability. A thematic analysis was conducted, selecting studies focused on ARAS in adverse weather conditions based on specific selection criteria. The analysis shows that while ARAS offers substantial safety benefits, its accuracy declines in challenging environments. Existing datasets, algorithms, and techniques were reviewed to identify the most effective options for ARAS applications. However, more comprehensive weather-resilient datasets and adaptive multi-sensor fusion approaches are still needed. Advancing in these areas will be critical to improving the robustness of ARAS and ensuring safer riding experiences across diverse environmental conditions. Full article
(This article belongs to the Special Issue Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety in Transportation)
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