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

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

Deadline for manuscript submissions: closed (15 December 2025) | Viewed by 18237

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Dr. Eva Michelaraki

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Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Iroon Polytechniou Str., 157 73 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

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Guest Editor

Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Iroon Polytechniou Str., 157 73 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 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

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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Published Papers (12 papers)

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Research

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25 pages, 13461 KB

Open AccessArticle

3D Environment Generation from Sparse Inputs for Automated Driving Function Development

by Till Temmen, Jasper Debougnoux, Li Li, Björn Krautwig, Tobias Brinkmann, Markus Eisenbarth and Jakob Andert

Vehicles 2026, 8(3), 47; https://doi.org/10.3390/vehicles8030047 - 2 Mar 2026

Viewed by 546

Abstract

The development of AI-driven automated driving functions requires vast amounts of diverse, high-quality data to ensure road safety and reliability. However, both the manual collection of real-world data and creation of 3D environments are costly, time-consuming, and hard to scale. Most automatic environment generation methods still rely heavily on manual effort, and only a few are tailored for Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) training and validation. We propose an automated generative framework that learns ground-truth features to reconstruct 3D environments from a road definition and two simple parameters for country and area type. Environment generation is structured into three modules—map-based data generation, semantic city generation, and final detailing. The overall framework is validated by training a perception network on a mixed set of real and synthetic data, validating it solely on real data, and comparing performance to assess the practical value of the environments we generated. By constructing a Pareto front over combinations of training set sizes and real-to-synthetic data ratios, we show that our synthetic data can replace up to

85 %

of real data without significant quality degradation. Our results demonstrate how multi-layered environment generation frameworks enable flexible and scalable data generation for perception tasks while incorporating ground-truth 3D environment data. This reduces reliance on costly field data and supports automated rapid scenario exploration for finding safety-critical edge cases. 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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30 pages, 6249 KB

Open AccessArticle

Modeling and Optimization Research on the Location Selection of Taxi Charging Stations in Severe Cold Areas

by Jiashuo Xu, Chunguang He, Ya Duan, Yazan Mualla, Mahjoub Dridi and Abdeljalil Abbas-Turki

Vehicles 2026, 8(2), 38; https://doi.org/10.3390/vehicles8020038 - 13 Feb 2026

Viewed by 433

Abstract

Decarbonizing the transport sector is crucial for achieving global carbon peaking and carbon neutrality goals. Electric taxis (e-taxis), which play a vital role in urban public transportation, are central to this transition. However, their operational performance deteriorates significantly under extremely cold conditions. Existing planning models for charging infrastructure often overlook the impact of low temperatures, creating a critical research gap. To address this issue, we propose a novel planning framework using Urumqi, China (43.8° N, 87.6° E) as a case study. Urumqi is a major cold-region metropolis, where January temperatures regularly drop below

- 20

°C. Our methodology includes two key steps: integrating 412 driver questionnaires and 1.2 million high-resolution GPS trajectories to extract temperature-sensitive charging demand profiles; and incorporating these profiles into an integer linear programming (ILP) model to minimize lifecycle costs, considering climatic constraints, taxi operation patterns, and grid limitations. A key innovation is a temperature-correction coefficient, which dynamically adjusts vehicle energy consumption and driving range based on ambient temperature. Results show superiority over conventional (temperature-ignoring) and random plans: 14-fold lower annualized cost, 23-fold shorter average queuing time, 96.2% high-frequency demand coverage (+16.6%), and 78% charging station utilization (+50.0%). It achieves 29.8–32.3% cost savings at

- 5

°C (over 25.9% even at

- 35

°C) and scales stably for 5–50% e-taxi penetration, offering a transferable framework for cold-region e-taxi charging optimization. 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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36 pages, 4643 KB

Open AccessArticle

System Readiness Assessment for Emerging Multimodal Mobility Systems Using a Hybrid Qualitative–Quantitative Framework

by Fabiana Carrión, Gregorio Romero, Jose-Manuel Mira and Jesus Félez

Vehicles 2026, 8(2), 35; https://doi.org/10.3390/vehicles8020035 - 9 Feb 2026

Viewed by 1156

Abstract

This paper presents a hybrid qualitative–quantitative framework for assessing the technical feasibility and system readiness of emerging multimodal mobility concepts, with specific application to the Pods4Rail project. The methodology integrates expert-based Technology Readiness Level (TRL) assessment with a probabilistic System Readiness Level (SRL) estimation that incorporates uncertainties in both TRLs and Integration Readiness Levels (IRLs). The qualitative component uses expert judgment and visual heat maps to identify subsystem-specific maturity gaps, particularly in automation, digitalization, and sustainability. The quantitative component explicitly separates three methodological layers often treated implicitly in prior research: (i) the probabilistic model representing uncertainties in TRL and IRL, (ii) the uncertainty-propagation problem linking these variables to system-level readiness, and (iii) the Monte Carlo algorithm employed to solve this problem. This structure enables the derivation of SRL distributions that reflect uncertainty more realistically than deterministic approaches, allowing statistical analysis of different characteristics of these distributions and exploratory sensitivity analysis. Results show that the Pods4Rail system is positioned between SRL 1 and SRL 2, corresponding to concept refinement and technology development stages. While hardware-related subsystems such as the Transport Unit and Rail Carrier Unit exhibit relatively higher maturity, planning, logistics, and operational management functionalities remain at early development stages. By combining interpretative insight with statistical rigor, the proposed framework offers a transparent and reproducible approach to early-phase readiness assessment. Its transferability makes it suitable for other innovative mobility systems facing similar challenges of incomplete information, uncertain integration pathways, and high conceptual complexity. 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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19 pages, 2621 KB

Open AccessArticle

Electric Vehicles to Support Grid Needs: Evidence from a Medium-Sized City

by Antonio Comi, Eskindir Ayele Atumo and Elsiddig Elnour

Vehicles 2026, 8(2), 30; https://doi.org/10.3390/vehicles8020030 - 4 Feb 2026

Viewed by 605

Abstract

Vehicle-to-grid (V2G) services are gaining attention as a strategy to integrate electric vehicles (EVs) into sustainable energy systems. Although technological aspects have been widely studied, methodologies for identifying optimal V2G hubs and forecasting the energy available for grid transfer remain limited. This study introduces a data-driven approach to (i) identify the optimal V2G region based on the aggregated parking duration using floating car data (FCD; collected from GPS-enabled vehicles); (ii) estimate the surplus battery capacity of electric vehicles in that region; and (iii) forecast the energy transferable to the grid. The methodology applies spatial k-means clustering to define candidate zones, computes aggregated parking durations, and selects the optimal hub. The surplus energy is estimated considering the daily mobility needs of users, 20% reserve, and transfer rates. For forecasting, autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models are implemented and compared. The proposed methodology has been applied to a real case study, using 58 days of FCD observations. The empirical findings of this study show the goodness of the proposed methodology, and the opportunity offered V2G technology to support the sustainable use of energy. The ARIMA model demonstrated a superior forecasting performance with an RMSE of 52.424, MAE of 36.05, and MAPE of 12.98%, outperforming LSTM (RMSE of 99.09, MAE of 80.351, and MAPE of 53.20%) under the current data conditions. The results of this study suggest that for supporting grid needs of a medium-sized city, V2G plays a key role, and at the current status of the EV penetration, the use of FCD and predictive approaches is paramount for making an informed decision. 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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19 pages, 5725 KB

Open AccessArticle

Real-Time 3D Scene Understanding for Road Safety: Depth Estimation and Object Detection for Autonomous Vehicle Awareness

by Marcel Simeonov, Andrei Kurdiumov and Milan Dado

Vehicles 2026, 8(2), 28; https://doi.org/10.3390/vehicles8020028 - 2 Feb 2026

Viewed by 941

Abstract

Accurate depth perception is vital for autonomous driving and roadside monitoring. Traditional stereo vision methods are cost-effective but often fail under challenging conditions such as low texture, reflections, or complex lighting. This work presents a perception pipeline built around FoundationStereo, a Transformer-based stereo depth estimation model. At low resolutions, FoundationStereo achieves real-time performance (up to 26 FPS) on embedded platforms like NVIDIA Jetson AGX Orin with TensorRT acceleration and power-of-two input sizes, enabling deployment in roadside cameras and in-vehicle systems. For Full HD stereo pairs, the same model delivers dense and precise environmental scans, complementing LiDAR while maintaining a high level of accuracy. YOLO11 object detection and segmentation is deployed in parallel for object extraction. Detected objects are removed from depth maps generated by FoundationStereo prior to point cloud generation, producing cleaner 3D reconstructions of the environment. This approach demonstrates that advanced stereo networks can operate efficiently on embedded hardware. Rather than replacing LiDAR or radar, it complements existing sensors by providing dense depth maps in situations where other sensors may be limited. By improving depth completeness, robustness, and enabling filtered point clouds, the proposed system supports safer navigation, collision avoidance, and scalable roadside infrastructure scanning for autonomous 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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14 pages, 458 KB

Open AccessArticle

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

Cited by 1 | Viewed by 812

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 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

Open AccessArticle

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 1279

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 3414

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1572

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 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

Open AccessArticle

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 1070

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 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

Open AccessArticle

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 2 | Viewed by 1997

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 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

Open AccessReview

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 2 | Viewed by 3178

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 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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