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Vehicles, Volume 7, Issue 1 (March 2025) – 13 articles

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23 pages, 5015 KiB  
Article
Barriers and Benefits: Understanding Riders’ Views on Pooled Rideshare in the U.S.
by Rakesh Gangadharaiah, Johnell Brooks, Lisa Boor, Kristin Kolodge and Yunyi Jia
Vehicles 2025, 7(1), 13; https://doi.org/10.3390/vehicles7010013 - 1 Feb 2025
Viewed by 258
Abstract
This manuscript provides actionable recommendations to enhance user satisfaction and address existing barriers regarding pooled rideshare (PR) in the United States. Despite PR’s intended benefits, such as reduced traffic congestion and cost savings, its adoption remains limited. To identify these actionable items, a [...] Read more.
This manuscript provides actionable recommendations to enhance user satisfaction and address existing barriers regarding pooled rideshare (PR) in the United States. Despite PR’s intended benefits, such as reduced traffic congestion and cost savings, its adoption remains limited. To identify these actionable items, a U.S. nationwide survey with 5385 participants explored transportation preferences, barriers, and motivators for PR use in the summer of 2021. First, two factor analyses were conducted. The first factor analysis identified the five factors associated with one’s willingness to consider PR (time/cost, traffic/environment, safety, privacy, and service experience). The second factor analysis revealed the four factors related to ways to optimize one’s PR experience (comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety). Privacy concerns, for instance, were found to reduce the likelihood of PR adoption by 77%, and convenience had the potential to increase it by 156%. A structural equation model evaluated the relationships among these nine key factors influencing PR usage to develop the Pooled Rideshare Acceptance Model (PRAM). The privacy, safety, trust service, and convenience factors each had a significant large effect (Cohen’s f2 > 0.35) on the model. PRAM was extended using multigroup analyses to reveal the nuanced impact of 16 demographics, including gender, generation, rideshare experience, etc., highlighting the need for tailored strategies to improve PR acceptance through the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMAs). Multiple workshops were held with diverse audiences to translate the team’s findings to date into 84 actionable recommendations, categorized across topical areas like safety, routing, driver and passenger selection, user education, etc. These findings are a foundation for a future study to determine which items resonate with different user groups. In the meantime, the actional items serve as a user-driven resource for policymakers, transportation network companies, and researchers, offering a roadmap to potential improvements to PR services to address existing concerns with the goal of increasing the usage of PR. Full article
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22 pages, 2446 KiB  
Article
Vehicle Localization in IoV Environments: A Vision-LSTM Approach with Synthetic Data Simulation
by Yi Liu, Jiade Jiang and Zijian Tian
Vehicles 2025, 7(1), 12; https://doi.org/10.3390/vehicles7010012 - 31 Jan 2025
Viewed by 282
Abstract
With the rapid development of the Internet of Vehicles (IoV) and autonomous driving technologies, robust and accurate visual pose perception has become critical for enabling smart connected vehicles. Traditional deep learning-based localization methods face persistent challenges in real-world vehicular environments, including occlusion, lighting [...] Read more.
With the rapid development of the Internet of Vehicles (IoV) and autonomous driving technologies, robust and accurate visual pose perception has become critical for enabling smart connected vehicles. Traditional deep learning-based localization methods face persistent challenges in real-world vehicular environments, including occlusion, lighting variations, and the prohibitive cost of collecting diverse real-world datasets. To address these limitations, this study introduces a novel approach by combining Vision-LSTM (ViL) with synthetic image data generated from high-fidelity 3D models. Unlike traditional methods reliant on costly and labor-intensive real-world data, synthetic datasets enable controlled, scalable, and efficient training under diverse environmental conditions. Vision-LSTM enhances feature extraction and classification performance through its matrix-based mLSTM modules and advanced feature aggregation strategy, effectively capturing both global and local information. Experimental evaluations in independent target scenes with distinct features and structured indoor environments demonstrate significant performance gains, achieving matching accuracies of 91.25% and 95.87%, respectively, and outperforming state-of-the-art models. These findings underscore the innovative advantages of integrating Vision-LSTM with synthetic data, highlighting its potential to overcome real-world limitations, reduce costs, and enhance accuracy and reliability for connected vehicle applications such as autonomous navigation and environmental perception. Full article
(This article belongs to the Special Issue Intelligent Connected Vehicles)
21 pages, 5752 KiB  
Article
Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm
by Sari Masri, Huthaifa I. Ashqar and Mohammed Elhenawy
Vehicles 2025, 7(1), 11; https://doi.org/10.3390/vehicles7010011 - 27 Jan 2025
Viewed by 687
Abstract
This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real time. [...] Read more.
This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real time. LLMs centralize traditionally disconnected traffic control processes and can integrate traffic data from diverse sources to provide context-aware decisions. LLMs can also deliver tailored outputs using various means such as wireless signals and visuals to drivers, infrastructures, and autonomous vehicles. To evaluate LLMs’ ability as traffic controllers, this study proposed a four-stage methodology. The methodology includes data creation and environment initialization, prompt engineering, conflict identification, and fine-tuning. We simulated multi-lane four-leg intersection scenarios and generated detailed datasets to enable conflict detection using LLMs and Python simulation as a ground truth. We used chain-of-thought prompts to lead LLMs in understanding the context, detecting conflicts, resolving them using traffic rules, and delivering context-sensitive traffic management solutions. We evaluated the performance of GPT-4o-mini, Gemini, and Llama as traffic controllers. Results showed that the fine-tuned GPT-mini achieved 83% accuracy and an F1-score of 0.84. The GPT-4o-mini model exhibited a promising performance in generating actionable traffic management insights, with high ROUGE-L scores across conflict identification of 0.95, decision making of 0.91, priority assignment of 0.94, and waiting time optimization of 0.92. This methodology confirmed LLMs’ benefits as a traffic controller in real-world applications. We demonstrated that LLMs can offer precise recommendations to drivers in real time including yielding, slowing, or stopping based on vehicle dynamics. This study demonstrates LLMs’ transformative potential for traffic control, enhancing efficiency and safety at intersections. Full article
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15 pages, 1041 KiB  
Review
Assessment of Road Vehicle Accident Approaches—A Review
by Irina Duma, Nicolae Burnete, Adrian Todoruț, Nicolae Cordoș, Cosmin-Constantin Danci and Alexandru Terec
Vehicles 2025, 7(1), 10; https://doi.org/10.3390/vehicles7010010 - 27 Jan 2025
Viewed by 554
Abstract
Given the complexity of the crashes and the increasing interest in public policies related to the reduction in both accidents and fatalities from road crashes, the proposed review of the specialty literature may serve as a starting point for individuals interested in developing [...] Read more.
Given the complexity of the crashes and the increasing interest in public policies related to the reduction in both accidents and fatalities from road crashes, the proposed review of the specialty literature may serve as a starting point for individuals interested in developing studies related to road vehicle accidents, reconstruction methodologies, assessment of vehicles crashworthiness, as well as evaluation of occupants’ behavior in different collision scenarios. Therefore, the present paper aims to offer a comprehensive overview of the specialty literature approaches in terms of road vehicle accidents through an analysis of the reconstruction methods used in the cases of vehicle-to-vehicle or vehicle-to-object crashes, as well as ways in which the crashworthiness of road vehicles is assessed by specialized organizations or individual experts. The addressed topics were summarized from a range of European and global strategies in the field of transportation, reports, testing protocols, as well as scientific research papers published in international databases. The main purpose of the present paper is to serve as a foundational resource for researchers and practitioners seeking to contextualize their work within a global framework. Full article
(This article belongs to the Special Issue Novel Solutions for Transportation Safety)
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17 pages, 4430 KiB  
Article
Enhancing Roundabout Efficiency Through Autonomous Vehicle Coordination
by Csaba Antonya, Calin Iclodean and Ioana-Alexandra Roșu
Vehicles 2025, 7(1), 9; https://doi.org/10.3390/vehicles7010009 - 24 Jan 2025
Viewed by 378
Abstract
The paper discusses the potential for autonomous vehicles to improve traffic flow on roundabouts, suggesting that their ability to slow down strategically can enhance traffic and reduce pollution on both main and yielding roads. A traffic simulator for a roundabout was developed for [...] Read more.
The paper discusses the potential for autonomous vehicles to improve traffic flow on roundabouts, suggesting that their ability to slow down strategically can enhance traffic and reduce pollution on both main and yielding roads. A traffic simulator for a roundabout was developed for a busy intersection of a new city neighborhood. We consider that some of the cars are self-driving, and they are fully aware of the traffic scenario. By optimizing their speed and timing their speed reduction, these vehicles can help maintain a balance between the number and time of crossing vehicles on both the main and yielding roads. This study evaluates the effectiveness of the intervention, demonstrating that autonomous vehicles can significantly improve roundabout efficiency, reducing congestion and pollution. The application of genetic algorithms is highlighted as an effective optimization method to find the right autonomous vehicle’s timing and speed reduction ratio combination on the main road to enhance traffic efficiency. Full article
(This article belongs to the Special Issue Feature Papers on Advanced Vehicle Technologies)
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19 pages, 25413 KiB  
Article
No-Reference Image Quality Assessment with Moving Spectrum and Laplacian Filter for Autonomous Driving Environment
by Woongchan Nam, Taehyun Youn and Chunghun Ha
Vehicles 2025, 7(1), 8; https://doi.org/10.3390/vehicles7010008 - 21 Jan 2025
Viewed by 541
Abstract
The increasing integration of autonomous driving systems into modern vehicles heightens the significance of Image Quality Assessment (IQA), as it pertains directly to vehicular safety. In this context, the development of metrics that can emulate the Human Visual System (HVS) in assessing image [...] Read more.
The increasing integration of autonomous driving systems into modern vehicles heightens the significance of Image Quality Assessment (IQA), as it pertains directly to vehicular safety. In this context, the development of metrics that can emulate the Human Visual System (HVS) in assessing image quality assumes critical importance. Given that blur is often the primary aberration in images captured by aging or deteriorating camera sensors, this study introduces a No-Reference (NR) IQA model termed BREMOLA (Blind/Referenceless Model via Moving Spectrum and Laplacian Filter). This model is designed to sensitively respond to varying degrees of blur in images. BREMOLA employs the Fourier transform to quantify the decline in image sharpness associated with increased blur. Subsequently, deviations in the Fourier spectrum arising from factors such as nighttime lighting or the presence of various objects are normalized using the Laplacian filter. Experimental application of the BREMOLA model demonstrates its capability to differentiate between images processed with a 3 × 3 average filter and their unprocessed counterparts. Additionally, the model effectively mitigates the variance introduced in the Fourier spectrum due to variables like nighttime conditions, object count, and environmental factors. Thus, BREMOLA presents a robust approach to IQA in the specific context of autonomous driving systems. Full article
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13 pages, 4126 KiB  
Article
Developing an Algorithm Limiting the Longitudinal Acceleration of an Electric Vehicle
by Akop Antonyan, Aleksandr Klimov, Andrey Buchkin, Andrey Keller, Sergey Shadrin, Daria Makarova and Yury Furletov
Vehicles 2025, 7(1), 7; https://doi.org/10.3390/vehicles7010007 - 21 Jan 2025
Viewed by 438
Abstract
The electric traction drive is increasingly being applied as a device providing traction force on driving wheels. This is due to its reliable torque transmission to the driving wheels, step-less regulation of the traction force on the driving wheels depending on the driving [...] Read more.
The electric traction drive is increasingly being applied as a device providing traction force on driving wheels. This is due to its reliable torque transmission to the driving wheels, step-less regulation of the traction force on the driving wheels depending on the driving conditions, and increased design capabilities. In terms of power, the electric traction drive has maximum torque at low speeds, which internal combustion engines lack. This property of the electric drive is not applied in urban vehicles, as not all passengers are comfortable with intensive acceleration. In modern vehicles with an electric traction drive, the maximum acceleration can be limited by software, which is the focus of this study. This paper aims to develop an algorithm capable of recognizing when the permissible longitudinal acceleration exceeds the limit and generating an action to maintain the acceptable acceleration level. The electric traction drive of a large-class electric bus was used as a control object. An algorithm and a control law are hereby developed, which reduce longitudinal acceleration using PI control. Both simulation modeling and full-scale tests on the electric bus were carried out to evaluate the performance and efficiency of the algorithm. In this paper, the authors also introduce the cumulative velocity concept and prove the operability and efficiency of the developed method. Full article
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23 pages, 7213 KiB  
Article
Advanced Adaptive Rule-Based Energy Management for Hybrid Energy Storage Systems (HESSs) to Enhance the Driving Range of Electric Vehicles
by Chew Kuew Wai, Taha Sadeq and Lee Cheun Hau
Vehicles 2025, 7(1), 6; https://doi.org/10.3390/vehicles7010006 - 18 Jan 2025
Viewed by 485
Abstract
The energy storage system (ESS) plays a crucial role in electric vehicles (EVs), impacting their performance and efficiency. While batteries are the standard choice for energy storage, they come with drawbacks like low power density and limited life cycles, which can hinder pure [...] Read more.
The energy storage system (ESS) plays a crucial role in electric vehicles (EVs), impacting their performance and efficiency. While batteries are the standard choice for energy storage, they come with drawbacks like low power density and limited life cycles, which can hinder pure battery electric vehicles (PBEVs). To address these issues, a hybrid energy storage system (HESS) that combines a battery with a supercapacitor provides a more effective solution. The battery delivers consistent power, while the supercapacitor manages peak power demands and regenerative braking energy. This study proposes a new energy management strategy for the HESS, an advanced adaptive rule-based algorithm. The results of the standard rule-based and adaptive rule-based algorithms are used to verify the proposed control algorithm. The system was modeled in MATLAB/Simulink and evaluated across three driving cycles—UDDS, NYCC, and Japan1015—while varying states of charge for the supercapacitors. The findings indicate that the HESS significantly alleviates battery stress compared to a pure battery system, enhancing both efficiency and lifespan. Among the algorithms tested, the advanced adaptive rule-based algorithm yielded the best results, increasing the number of viable drive cycles. Full article
(This article belongs to the Topic Electric Vehicles Energy Management, 2nd Volume)
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17 pages, 2590 KiB  
Article
Analyzing Crash Severity: Human Injury Severity Prediction Method Based on Transformer Model
by Yalan Jiang, Xianguo Qu, Weiwei Zhang, Wenfeng Guo, Jiejie Xu, Wangpengfei Yu and Yang Chen
Vehicles 2025, 7(1), 5; https://doi.org/10.3390/vehicles7010005 - 15 Jan 2025
Viewed by 579
Abstract
Traffic accident-related injuries and fatalities are a serious global public health and social development challenge. The accurate prediction of crash severity improves road safety and reduces casualties, as well as serving road managers and policy makers. Prediction models need to learn and analyze [...] Read more.
Traffic accident-related injuries and fatalities are a serious global public health and social development challenge. The accurate prediction of crash severity improves road safety and reduces casualties, as well as serving road managers and policy makers. Prediction models need to learn and analyze the various characteristic factors of traffic accidents and capture from them the inherent complex relationship between accident characteristics and the severity of traffic accidents. However, most accident prediction studies lack analytical predictions of injury severity, and predictive models rely on the content and quality of accident datasets. To increase the robustness and accuracy of prediction models, this paper leverages a Transformer-based architecture for the severity prediction of traffic collisions from human injury severity. This framework learns both text and sequence data from accident datasets. After comparative analysis, the framework can achieve the prediction of human injury severity under different data categories and show good prediction performance at low injury severity levels using only textual data or sequence data. Full article
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25 pages, 3488 KiB  
Article
Emerging Decision-Making for Transportation Safety: Collaborative Agent Performance Analysis
by Jack Maguire-Day, Saba Al-Rubaye, Anirudh Warrier, Muhammet A. Sen, Huw Whitworth and Mohammad Samie
Vehicles 2025, 7(1), 4; https://doi.org/10.3390/vehicles7010004 - 15 Jan 2025
Viewed by 861
Abstract
This paper addresses the challenge of improving decision-making capabilities and safety in autonomous vehicles (AVs) using Agent-Based Modelling (ABM). The study evaluates ABM’s effect on Advanced Driver Assistance Systems (ADASs) in challenging driving situations, like lane merging, by incorporating it into a simulation [...] Read more.
This paper addresses the challenge of improving decision-making capabilities and safety in autonomous vehicles (AVs) using Agent-Based Modelling (ABM). The study evaluates ABM’s effect on Advanced Driver Assistance Systems (ADASs) in challenging driving situations, like lane merging, by incorporating it into a simulation framework designed for autonomous vehicles. Identifying emergent behaviours that enhance safety and efficiency, verifying the efficacy of ABM in AV decision-making, and investigating the function of hardware acceleration to enable practical application in ADASs are some of the major achievements. According to the simulation results, ABM can greatly improve AV performance, providing a practical and scalable means of enhancing safety in future transportation systems. Full article
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25 pages, 6157 KiB  
Article
Early Driver Fatigue Detection System: A Cost-Effective and Wearable Approach Utilizing Embedded Machine Learning
by Chengyou Lin, Xinying Zhu, Renpeng Wang, Wei Zhou, Na Li and Yu Xie
Vehicles 2025, 7(1), 3; https://doi.org/10.3390/vehicles7010003 - 8 Jan 2025
Viewed by 636
Abstract
Driving fatigue is the cause of many traffic accidents and poses a serious threat to road safety. To address this issue, this paper aims to develop a system for the early detection of driver fatigue. The system leverages heart rate variability (HRV) features [...] Read more.
Driving fatigue is the cause of many traffic accidents and poses a serious threat to road safety. To address this issue, this paper aims to develop a system for the early detection of driver fatigue. The system leverages heart rate variability (HRV) features and embedded machine learning to estimate the driver’s fatigue level. The driver’s HRV is derived from electrocardiogram (ECG) signals captured by a wearable device for analysis. Time- and frequency-domain HRV features are then extracted and used as the input for a machine learning classifier. A dataset of HRV features is collected from a driving simulation experiment involving 18 participants. Four machine learning classifiers are evaluated, and a backpropagation neural network (BPNN) is selected for its superior performance, achieving up to 94.35% accuracy. The optimized classifier is successfully deployed on an embedded system, providing a cost-effective and portable solution for the early detection of driver fatigue. The results demonstrate the feasibility of using HRV-based machine learning models for the early detection of driver fatigue, contributing to enhanced road safety and a reduced accident risk. Full article
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25 pages, 3292 KiB  
Article
Lane Detection Based on CycleGAN and Feature Fusion in Challenging Scenes
by Eric Hsueh-Chan Lu and Wei-Chih Chiu
Vehicles 2025, 7(1), 2; https://doi.org/10.3390/vehicles7010002 - 1 Jan 2025
Viewed by 549
Abstract
Lane detection is a pivotal technology of the intelligent driving system. By identifying the position and shape of the lane, the vehicle can stay in the correct lane and avoid accidents. Image-based deep learning is currently the most advanced method for lane detection. [...] Read more.
Lane detection is a pivotal technology of the intelligent driving system. By identifying the position and shape of the lane, the vehicle can stay in the correct lane and avoid accidents. Image-based deep learning is currently the most advanced method for lane detection. Models using this method already have a very good recognition ability in general daytime scenes, and can almost achieve real-time detection. However, these models often fail to accurately identify lanes in challenging scenarios such as night, dazzle, or shadows. Furthermore, the lack of diversity in the training data restricts the capacity of the models to handle different environments. This paper proposes a novel method to train CycleGAN with existing daytime and nighttime datasets. This method can extract features of different styles and multi-scales, thereby increasing the richness of model input. We use CycleGAN as a domain adaptation model combined with an image segmentation model to boost the model’s performance in different styles of scenes. The proposed consistent loss function is employed to mitigate performance disparities of the model in different scenarios. Experimental results indicate that our method enhances the detection performance of original lane detection models in challenging scenarios. This research helps improve the dependability and robustness of intelligent driving systems, ultimately making roads safer and enhancing the driving experience. Full article
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34 pages, 2061 KiB  
Review
Towards Energy Efficiency: Innovations in High-Frequency Converters for Renewable Energy Systems and Electric Vehicles
by Paul Arévalo, Danny Ochoa-Correa and Edisson Villa-Ávila
Vehicles 2025, 7(1), 1; https://doi.org/10.3390/vehicles7010001 - 30 Dec 2024
Viewed by 956
Abstract
This study reviews advancements in high-frequency converters for renewable energy systems and electric vehicles, emphasizing their role in enhancing energy efficiency and sustainability. Using the PRISMA 2020 methodology, 73 high-quality studies from 2014 to 2024 were synthesized to evaluate innovative designs, advanced materials, [...] Read more.
This study reviews advancements in high-frequency converters for renewable energy systems and electric vehicles, emphasizing their role in enhancing energy efficiency and sustainability. Using the PRISMA 2020 methodology, 73 high-quality studies from 2014 to 2024 were synthesized to evaluate innovative designs, advanced materials, control strategies, and future opportunities. Key findings reveal significant progress in converter topologies, such as dual active bridge and LLC resonant designs, which enhance efficiency and scalability through soft-switching. Wide-bandgap semiconductors, including silicon carbide and gallium nitride, have driven improvements in power density, thermal management, and compactness. Advanced control strategies, including adaptive and AI-driven methods, enhance stability and efficiency in microgrids and vehicle-to-grid systems. Applications in photovoltaic and wind energy systems demonstrate the converters’ impact on improving energy conversion and system reliability. Future opportunities focus on hybrid and multifunctional designs that integrate renewable energy, storage, and electric mobility with intelligent control technologies like digital twins and AI. These innovations highlight the transformative potential of high-frequency converters in addressing global energy challenges driving sustainable energy and transportation solutions. This review offers critical insights into current advancements and pathways for further research and development in this field. Full article
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