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Keywords = automated vehicle (AV)

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15 pages, 1306 KiB  
Article
Risk Perception in Complex Systems: A Comparative Analysis of Process Control and Autonomous Vehicle Failures
by He Wen, Zaman Sajid and Rajeevan Arunthavanathan
AI 2025, 6(8), 164; https://doi.org/10.3390/ai6080164 - 22 Jul 2025
Viewed by 387
Abstract
Background: As intelligent systems increasingly operate in high-risk environments, understanding how they perceive and respond to hazards is critical for ensuring safety. Methods: In this study, we conduct a comparative analysis of 60 real-world accident reports, 30 from process control systems (PCSs) and [...] Read more.
Background: As intelligent systems increasingly operate in high-risk environments, understanding how they perceive and respond to hazards is critical for ensuring safety. Methods: In this study, we conduct a comparative analysis of 60 real-world accident reports, 30 from process control systems (PCSs) and 30 from autonomous vehicles (AVs), to examine differences in risk triggers, perception paradigms, and interaction failures between humans and artificial intelligence (AI). Results: Our findings reveal that PCS risks are predominantly internal to the system and detectable through deterministic, rule-based mechanisms, whereas AVs’ risks are externally driven and managed via probabilistic, multi-modal sensor fusion. More importantly, despite these architectural differences, both domains exhibit recurring human–AI interaction failures, including over-reliance on automation, mode confusion, and delayed intervention. In the case of PCSs, these failures are historically tied to human–automation interaction; this article extrapolates these patterns to anticipate potential human–AI interaction challenges as AI adaptation increases. Conclusions: This study highlights the need for a hybrid risk perception framework and improved human-centered design to enhance situational awareness and responsiveness. While AI has not yet been implemented in PCS incident studies, this work interprets human–automation failures in these cases as indicative of potential challenges in human–AI interaction that may arise in future AI-integrated process systems. Implications extend to developing safer intelligent systems across industrial and transportation sectors. Full article
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30 pages, 2282 KiB  
Article
User Experience of Navigating Work Zones with Automated Vehicles: Insights from YouTube on Challenges and Strengths
by Melika Ansarinejad, Kian Ansarinejad, Pan Lu and Ying Huang
Smart Cities 2025, 8(4), 120; https://doi.org/10.3390/smartcities8040120 - 19 Jul 2025
Viewed by 426
Abstract
Understanding automated vehicle (AV) behavior in complex road environments and user attitudes in such contexts is critical for their safe and effective integration into smart cities. Despite growing deployment, limited public data exist on AV performance in construction zones; highly dynamic settings marked [...] Read more.
Understanding automated vehicle (AV) behavior in complex road environments and user attitudes in such contexts is critical for their safe and effective integration into smart cities. Despite growing deployment, limited public data exist on AV performance in construction zones; highly dynamic settings marked by irregular lane markings, shifting detours, and unpredictable human presence. This study investigates AV behavior in these conditions through qualitative, video-based analysis of user-documented experiences on YouTube, focusing on Tesla’s supervised Full Self-Driving (FSD) and Waymo systems. Spoken narration, captions, and subtitles were examined to evaluate AV perception, decision-making, control, and interaction with humans. Findings reveal that while AVs excel in structured tasks such as obstacle detection, lane tracking, and cautious speed control, they face challenges in interpreting temporary infrastructure, responding to unpredictable human actions, and navigating low-visibility environments. These limitations not only impact performance but also influence user trust and acceptance. The study underscores the need for continued technological refinement, improved infrastructure design, and user-informed deployment strategies. By addressing current shortcomings, this research offers critical insights into AV readiness for real-world conditions and contributes to safer, more adaptive urban mobility systems. Full article
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18 pages, 3850 KiB  
Article
Operational Evaluation of Mixed Flow on Highways Considering Trucks and Autonomous Vehicles Based on an Improved Car-Following Decision Framework
by Nan Kang, Chun Qian, Yiyan Zhou and Wenting Luo
Sustainability 2025, 17(14), 6450; https://doi.org/10.3390/su17146450 - 15 Jul 2025
Viewed by 342
Abstract
This study proposes a new method to improve the accuracy of car-following models in predicting the mobility of mixed traffic flow involving trucks and automated vehicles (AVs). A classification is developed to categorize car-following behaviors into eight distinct modes based on vehicle type [...] Read more.
This study proposes a new method to improve the accuracy of car-following models in predicting the mobility of mixed traffic flow involving trucks and automated vehicles (AVs). A classification is developed to categorize car-following behaviors into eight distinct modes based on vehicle type (passenger car/truck) and autonomy level (human-driven vehicle [HDV]/AV) for parameter calibration and simulation. The car-following model parameters are calibrated based on the HighD dataset, and the models are selected through minimizing statistical error. A cellular-automaton-based simulation platform is implemented in MATLAB (R2023b), and a decision framework is developed for the simulation. Key findings demonstrate that mode-specific parameter calibration improves model accuracy, achieving an average error reduction of 80% compared to empirical methods. The simulation results reveal a positive correlation between the AV penetration rate and traffic flow stability, which consequently enhances capacity. Specifically, a full transition from 0% to 100% AV penetration increases traffic capacity by 50%. Conversely, elevated truck penetration rates degrade traffic flow stability, reducing the average speed by 75.37% under full truck penetration scenarios. Additionally, higher AV penetration helps stabilize traffic flow, leading to reduced speed fluctuations and lower emissions, while higher truck proportions contribute to higher emissions due to increased traffic instability. Full article
(This article belongs to the Section Sustainable Transportation)
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27 pages, 6174 KiB  
Article
Non-Compliant Behaviour of Automated Vehicles in a Mixed Traffic Environment
by Marlies Mischinger-Rodziewicz, Felix Hofbaur, Michael Haberl and Martin Fellendorf
Appl. Sci. 2025, 15(14), 7852; https://doi.org/10.3390/app15147852 - 14 Jul 2025
Viewed by 202
Abstract
Legal requirements for minimum distances between vehicles are often not met for short periods of time, especially when changing lanes on multi-lane roads. These situations are typically non-hazardous, as human drivers anticipate surrounding traffic, allowing for shorter headways and improved traffic flow. Automated [...] Read more.
Legal requirements for minimum distances between vehicles are often not met for short periods of time, especially when changing lanes on multi-lane roads. These situations are typically non-hazardous, as human drivers anticipate surrounding traffic, allowing for shorter headways and improved traffic flow. Automated vehicles (AVs), however, are typically designed to maintain strict headway limits, potentially reducing traffic efficiency. Therefore, legal questions arise as to whether mandatory gap and headway limits for AVs may be violated during periods of non-compliance. While traffic flow simulation is a common method for analyzing AV impacts, previous studies have typically modeled AV behavior using driver models originally designed to replicate human driving. These models are not well suited for representing clearly defined, structured non-compliant maneuvers, as they cannot simulate intentional, rule-deviating strategies. This paper addresses this gap by introducing a concept for AV non-compliant behavior and implementing it as a module within a pre-existing AV driver model. Simulations were conducted on a three-lane highway with an on-ramp under varying traffic volumes and AV penetration rates. The results showed that, with an AV-penetration rate of more than 25%, road capacity at highway entrances could be increased and travel times reduced by over 20%, provided that AVs were allowed to merge with a legal gap of 0.9 s and a minimum non-compliant gap of 0.6 s lasting up to 3 s. This suggests that performance gains are achievable under adjusted legal requirements. In addition, the proposed framework can serve as a foundation for further development of AV driver models aiming at improving traffic efficiency while maintaining regulatory compliance. Full article
(This article belongs to the Section Transportation and Future Mobility)
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27 pages, 1431 KiB  
Article
Environmental and Behavioral Dimensions of Private Autonomous Vehicles in Sustainable Urban Mobility
by Iulia Ioana Mircea, Eugen Rosca, Ciprian Sorin Vlad and Larisa Ivascu
Clean Technol. 2025, 7(3), 56; https://doi.org/10.3390/cleantechnol7030056 - 7 Jul 2025
Viewed by 461
Abstract
In the current context, where environmental concerns are gaining increased attention, the transition toward sustainable urban mobility stands out as a necessary and responsible step. Technological advancements over the past decade have brought private autonomous vehicles, particularly those defined by the Society of [...] Read more.
In the current context, where environmental concerns are gaining increased attention, the transition toward sustainable urban mobility stands out as a necessary and responsible step. Technological advancements over the past decade have brought private autonomous vehicles, particularly those defined by the Society of Automotive Engineers Levels 4 and 5, into focus as promising solutions for mitigating road congestion and reducing greenhouse gas emissions. However, the extent to which Autonomous Vehicles can fulfill this potential depends largely on user acceptance, patterns of use, and their integration within broader green energy and sustainability policies. The present paper aims to develop an integrated conceptual model that links behavioral determinants to environmental outcomes, assessing how individuals’ intention to adopt private autonomous vehicles can contribute to sustainable urban mobility. The model integrates five psychosocial determinants—perceived usefulness, trust in technology, social influence, environmental concern, and perceived behavioral control—with contextual variables such as energy source, infrastructure availability, and public policy. These components interact to predict users’ intention to adopt AVs and their perceived contribution to urban sustainability. Methodologically, the study builds on a narrative synthesis of the literature and proposes a framework applicable to empirical validation through structural equation modeling (SEM). The model draws on established frameworks such as Technology Acceptance Model (TAM), Theory of Planned Behavior, and Unified Theory of Acceptance and Use of Technology, incorporating constructs including perceived usefulness, trust in technology, social influence, environmental concern, and perceived behavioral control, constructs later to be examined in relation to key contextual variables, including the energy source powering Autonomous Vehicles—such as electricity from mixed or renewable grids, hydrogen, or hybrid systems—and the broader policy environment (regulatory frameworks, infrastructure investment, fiscal incentives, and alignment with climate and mobility strategies and others). The research provides relevant directions for public policy and behavioral interventions in support of the development of clean and smart urban transport in the age of automation. Full article
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23 pages, 1575 KiB  
Article
An Integrated Blockchain Framework for Secure Autonomous Vehicle Communication System
by Juan de Anda-Suárez, José Luis López-Ramírez, Daniel Jimenez-Mendoza, José Manuel Benitez-Quintero, Eli Gabriel Avina-Bravo, David Asael Gutierrez-Hernandez and Juan Gabriel Avina-Cervantes
Information 2025, 16(7), 557; https://doi.org/10.3390/info16070557 - 30 Jun 2025
Viewed by 484
Abstract
Autonomous Vehicles (AV) have been extensively studied in both scientific and social contexts. Over the past two decades, there has been a significant rise in their real-world applications, including neural networks, Blockchain, Internet of Things, autonomous navigation, computer vision, automation processes, and various [...] Read more.
Autonomous Vehicles (AV) have been extensively studied in both scientific and social contexts. Over the past two decades, there has been a significant rise in their real-world applications, including neural networks, Blockchain, Internet of Things, autonomous navigation, computer vision, automation processes, and various other areas. Hence, it is imperative to investigate the interplay between software, hardware, and individuals. To guarantee secure and unaffected interactions within autonomous vehicle devices and networks, decentralized Blockchain technology is proposed. This study presents the introduction of a framework we named “DEMU-NAV” for an ecosystem that includes Artificial Intelligence (AI), humans, and robots. The framework makes use of a decentralized Blockchain, Smart-Contract (SC), and Internet of things (IoT) network. Our framework was implemented using Ethereum and Python, enabling us to oversee Blockchain, Smart-Contracts, and the IoT for the facilitation of autonomous vehicle navigation. Full article
(This article belongs to the Special Issue Blockchain, Technology and Its Application)
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36 pages, 575 KiB  
Review
Automated Vehicles and People Living with a Disability: Opportunities, Challenges, and Future Directions for Sustainable Mobility
by Elsa Yousfi, Thomas Jacquet and Natacha Métayer
Sustainability 2025, 17(13), 5941; https://doi.org/10.3390/su17135941 - 27 Jun 2025
Viewed by 454
Abstract
This article reviews the current scientific literature that relates to automated vehicles (AVs), vehicles controlled by a computer instead of a human driver, and people living with a disability (PLWD). The aim of this review is (1) to explore how AVs might improve [...] Read more.
This article reviews the current scientific literature that relates to automated vehicles (AVs), vehicles controlled by a computer instead of a human driver, and people living with a disability (PLWD). The aim of this review is (1) to explore how AVs might improve mobility for PLWD, (2) to identify research gaps to guide future studies, and (3) to examine the real-world applicability of existing research. A structured search following PRISMA guidelines identified 66 relevant peer-reviewed publications. The findings suggest that AVs hold promise in reducing transport-related social exclusion by increasing autonomy, flexibility, and accessibility for PLWD, thereby supporting the transition toward more inclusive and environmentally sustainable transport systems. However, the potential benefits of AVs for the mobility of PLWD depend on the type of vehicle considered (e.g., private vs. public transport) as well as the potential challenges related to the legal framework, accessibility standards, and addressing PLWD concerns, opinions, and needs. To overcome the existing obstacles to the widespread adoption of AVs and make them a real opportunity for PLWD, collaboration between all stakeholders in the sector (i.e., governments, industries, and disability associations) is needed. This review supports cross-sector collaboration for inclusive AV implementation. Full article
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44 pages, 5969 KiB  
Article
iRisk: Towards Responsible AI-Powered Automated Driving by Assessing Crash Risk and Prevention
by Naomi Y. Mbelekani and Klaus Bengler
Electronics 2025, 14(12), 2433; https://doi.org/10.3390/electronics14122433 - 14 Jun 2025
Viewed by 712
Abstract
Advanced technology systems and neuroelectronics for crash risk assessment and anticipation may be a promising field for advancing responsible automated driving on urban roads. In principle, there are prospects of an artificially intelligent (AI)-powered automated vehicle (AV) system that tracks the degree of [...] Read more.
Advanced technology systems and neuroelectronics for crash risk assessment and anticipation may be a promising field for advancing responsible automated driving on urban roads. In principle, there are prospects of an artificially intelligent (AI)-powered automated vehicle (AV) system that tracks the degree of perceived crash risk (as either low, mid, or high) and perceived safety. As a result, communicating (verbally or nonverbally) this information to the user based on human factor aspects should be reflected. As humans and vehicle automation systems are prone to error, we need to design advanced information and communication technologies that monitor risks and act as a mediator when necessary. One possible approach is towards designing a crash risk classification and management system. This would be through responsible AI that monitors the user’s mental states associated with risk-taking behaviour and communicates this information to the user, in conjunction with the driving environment and AV states. This concept is based on a literature review and industry experts’ perspectives on designing advanced technology systems that support users in preventing crash risk encounters due to long-term effects. Equally, learning strategies for responsible automated driving on urban roads were designed. In a sense, this paper offers the reader a meticulous discussion on conceptualising a safety-inspired ‘ergonomically responsible AI’ concept in the form of an intelligent risk assessment system (iRisk) and an AI-powered Risk information Human–Machine Interface (AI rHMI) as a useful concept for responsible automated driving and safe human–automation interaction. Full article
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29 pages, 6913 KiB  
Article
Intersection Sight Distance in Mixed Automated and Conventional Vehicle Environments with Yield Control on Minor Roads
by Sean Sarran and Yasser Hassan
Smart Cities 2025, 8(3), 73; https://doi.org/10.3390/smartcities8030073 - 23 Apr 2025
Viewed by 468
Abstract
Intersection sight distance (ISD) requirements, currently designed for driver-operated vehicles (DVs), will be affected once automated vehicles (AVs) enter the driving environment. This paper examines the ISD for intersections with a yield control on a minor road in a mixed DV-AV environment. Five [...] Read more.
Intersection sight distance (ISD) requirements, currently designed for driver-operated vehicles (DVs), will be affected once automated vehicles (AVs) enter the driving environment. This paper examines the ISD for intersections with a yield control on a minor road in a mixed DV-AV environment. Five potential conflict types with different ISD requirements are modeled as a minor-road vehicle proceeds to cross the intersection, turns right, or turns left. Furthermore, different models are developed for each conflict type depending on the vehicle types on the minor and major roads. These models, along with the intersection geometry, establish the system demand and supply models for ISD reliability analysis. A surrogate safety measure is developed and used to measure ISD non-compliance and is denoted by the probability of unresolved conflicts (PUC). The models are applied to a case study intersection, where PUC values are estimated using Monte Carlo Simulation and compared to an established target value relating to the DV-only traffic of 0.00674. The results show that AV-related traffic has higher overall PUC values than those of DV-only traffic. A corrective measure, reducing the AV speed limit on the minor-road approaches by 3 to 4 km/h, decreases the overall PUC to values below those of the target PUC. Full article
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27 pages, 12352 KiB  
Article
Operationalizing Dyadic Urban Traffic Interaction Studies: From Theory to Practice
by Debargha Dey, Azra Habibovic and Wendy Ju
Appl. Sci. 2025, 15(7), 3738; https://doi.org/10.3390/app15073738 - 28 Mar 2025
Viewed by 543
Abstract
Realistically modeling interactions between road users—like those between drivers or between drivers and pedestrians—within experimental settings come with pragmatic challenges. Due to practical constraints, research typically focuses on a limited subset of potential scenarios, raising questions about the scalability and generalizability of findings [...] Read more.
Realistically modeling interactions between road users—like those between drivers or between drivers and pedestrians—within experimental settings come with pragmatic challenges. Due to practical constraints, research typically focuses on a limited subset of potential scenarios, raising questions about the scalability and generalizability of findings about interactions to untested scenarios. Here, we aim to tackle this by laying the methodological groundwork for defining representative scenarios for dyadic (two-actor) interactions that can be analyzed individually. This paper introduces a conceptual guide for operationalizing controlled dyadic traffic interaction studies, developed through extensive interdisciplinary brainstorming to bridge theoretical models and practical experimental design. It elucidates critical trade-offs in scenario selection, interaction approaches, measurement strategies, and timing coordination, thereby enhancing reproducibility and clarity for future traffic interaction research and streamlining the design process. The methodologies and insights we provide aim to enhance the accessibility and quality of traffic interaction research, offering a guide that aids researchers in setting up studies and ensures clarity and reproducibility in reporting, bridging the gap between theoretical traffic interaction models and practical applications in controlled experiments, thereby contributing to advancements in human factors research on traffic management and safety. Full article
(This article belongs to the Special Issue Human–Vehicle Interactions)
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27 pages, 10045 KiB  
Article
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding
by Mohammed Elhenawy, Huthaifa I. Ashqar, Andry Rakotonirainy, Taqwa I. Alhadidi, Ahmed Jaber and Mohammad Abu Tami
Electronics 2025, 14(7), 1282; https://doi.org/10.3390/electronics14071282 - 24 Mar 2025
Cited by 2 | Viewed by 3137
Abstract
Scene understanding is essential for enhancing driver safety, generating human-centric explanations for Automated Vehicle (AV) decisions, and leveraging Artificial Intelligence (AI) for retrospective driving video analysis. This study developed a dynamic scene retrieval system using Contrastive Language–Image Pretraining (CLIP) models, which can be [...] Read more.
Scene understanding is essential for enhancing driver safety, generating human-centric explanations for Automated Vehicle (AV) decisions, and leveraging Artificial Intelligence (AI) for retrospective driving video analysis. This study developed a dynamic scene retrieval system using Contrastive Language–Image Pretraining (CLIP) models, which can be optimized for real-time deployment on edge devices. The proposed system outperforms state-of-the-art in-context learning methods, including the zero-shot capabilities of GPT-4o, particularly in complex scenarios. By conducting frame-level analyses on the Honda Scenes Dataset, which contains a collection of about 80 h of annotated driving videos capturing diverse real-world road and weather conditions, our study highlights the robustness of CLIP models in learning visual concepts from natural language supervision. The results also showed that fine-tuning the CLIP models, such as ViT-L/14 (Vision Transformer) and ViT-B/32, significantly improved scene classification, achieving a top F1-score of 91.1%. These results demonstrate the ability of the system to deliver rapid and precise scene recognition, which can be used to meet the critical requirements of advanced driver assistance systems (ADASs). This study shows the potential of CLIP models to provide scalable and efficient frameworks for dynamic scene understanding and classification. Furthermore, this work lays the groundwork for advanced autonomous vehicle technologies by fostering a deeper understanding of driver behavior, road conditions, and safety-critical scenarios, marking a significant step toward smarter, safer, and more context-aware autonomous driving systems. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems and Sustainable Smart Cities)
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14 pages, 409 KiB  
Review
Automated Vehicles: Are Cities Ready to Adopt AVs as the Sustainable Transport Solution?
by Md Arifuzzaman and Shohel Amin
Sustainability 2025, 17(5), 2236; https://doi.org/10.3390/su17052236 - 4 Mar 2025
Cited by 2 | Viewed by 1405
Abstract
Cities are looking for an approach to affordable, integrated and sustainable transport systems across all transport modes and services. Automated vehicle (AV) technologies use emerging technologies to integrate multimodal transport systems and ensure sustainable mobility in a city. Vehicle automation has entered the [...] Read more.
Cities are looking for an approach to affordable, integrated and sustainable transport systems across all transport modes and services. Automated vehicle (AV) technologies use emerging technologies to integrate multimodal transport systems and ensure sustainable mobility in a city. Vehicle automation has entered the public conscious with several auto companies leading recent developments in legislation and affordable cars. Governments support AVs through policies and legal frameworks, and it is the responsibility of AV dealers to comply with legal and policy provisions so that the benefits of this new and promising industry can be felt. Despite the growing interest in AVs as a potential solution for sustainable transportation, several research gaps remain in relation to technology and infrastructure readiness, policy and regulation, equity and accessibility concerns, public acceptance and behaviour, and integration with public transport. This paper discusses the challenges and dilemmas of adopting AVs within the existing urban transportation system and within existing design standards in the United Kingdom and explores the progress and opportunities related to policies of transportation that may stem from the emergence of AV technologies in the UK. The potential of AVs is still limited by cyber insecurity, incompetent infrastructure, social acceptance, and public awareness. However, AVs are crucial to a city’s efficiency and prosperity and will become essential components for the provision of more flexible, convenient, integrated and sustainable travel options. Full article
(This article belongs to the Special Issue Smart Mobility for Sustainable Future Transportation)
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19 pages, 4046 KiB  
Article
Modeling Determinants of Autonomous Vehicle Utilization in Private and Shared Ownership Models
by Bradley W. Lane and Scott B. Kelley
Future Transp. 2025, 5(1), 18; https://doi.org/10.3390/futuretransp5010018 - 6 Feb 2025
Viewed by 1046
Abstract
Autonomous vehicles (AVs) and shared mobility constitute two of the “Three Revolutions” that portend major changes to surface transportation. AVs promise to reduce accidents, expand accessibility, and decrease congestion, while shared mobility provides the benefits of automotive transportation without requiring the purchase of [...] Read more.
Autonomous vehicles (AVs) and shared mobility constitute two of the “Three Revolutions” that portend major changes to surface transportation. AVs promise to reduce accidents, expand accessibility, and decrease congestion, while shared mobility provides the benefits of automotive transportation without requiring the purchase of a vehicle or the ability to drive it. Despite great promise to alleviate the negative externalities imposed by transportation, there remains much to be understood about the combined diffusion and impact of AVs and shared mobility. There is little demonstrated experience and application of AVs to the public, and how and where people would use automated shared mobility relative to their current travel is largely unknown. This study advances our understanding by utilizing an intercept survey of 232 respondents in Ann Arbor, Michigan who had made a discretionary trip to one of two central and two suburban locations. The novel approach of using intercept surveys allows us to gather more valid data about the willingness of respondents to replace the mode they just used for either a privately owned or a shared AV and do so for the trip purpose most conducive to using such a vehicle. We incorporate descriptive and spatial analyses and then utilize multinomial logit models to predict the factors influencing the encouragement or discouragement of substituting a private and a shared AV for their previous trip. We found that active mobility and transit trips work in competition with private AVs, while youth encourages interest. Meanwhile, active mobility, increasing age, and one of our measures of density discourage interest, while female respondents and the same measure of density increase interest. The results suggest that future efforts to facilitate the adoption of shared AVs target areas of the city that are relatively dense and residents in these areas where a shared AV would enhance individuals’ mobility. Full article
(This article belongs to the Special Issue Emerging Issues in Transport and Mobility)
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24 pages, 6732 KiB  
Article
Microscopic Simulation of Heterogeneous Traffic Flow on Multi-Lane Ring Roads and Highways
by Haizhen Li and Yongfeng Ju
Appl. Sci. 2025, 15(3), 1453; https://doi.org/10.3390/app15031453 - 31 Jan 2025
Cited by 1 | Viewed by 1045
Abstract
In the connected and autonomous vehicle (CAV) environment, vehicles with different levels of automation are being deployed on public roads. Most research focuses on traffic flow simulation for a single vehicle type, while there are few studies on the interactions of mixed traffic [...] Read more.
In the connected and autonomous vehicle (CAV) environment, vehicles with different levels of automation are being deployed on public roads. Most research focuses on traffic flow simulation for a single vehicle type, while there are few studies on the interactions of mixed traffic involving CAVs, autonomous vehicles (AVs), and human-driven vehicles (HDVs). To fill this gap, this study investigates the traffic performance of heterogeneous traffic on multi-lane ring roads and highways with on-ramps. Leveraging the Python and SUMO simulation platform, the JAD strategy is introduced to optimize the dynamic interactions within heterogeneous traffic flow. Various scenarios with different proportions of CAVs, AVs, and HDVs were simulated to assess their impact on traffic efficiency, dynamics, safety, and environmental factors. The findings indicate that traffic efficiency, stability, and environmental impact improve as the share of HDVs declines and the proportion of CAVs and AVs rises. In scenarios with more HDVs, the improvements are minimal. Traffic safety gradually improves as the proportion of CAVs and AVs increases, with significant improvements observed when CAVs account for 40% of vehicles on ring roads and 50% on highways. This study advances the understanding of complex interactions in mixed traffic scenarios and their implications for traffic management. Full article
(This article belongs to the Section Transportation and Future Mobility)
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28 pages, 1565 KiB  
Article
Promoting Sustainable Transportation: How People Trust and Accept Autonomous Vehicles—Focusing on the Different Levels of Collaboration Between Human Drivers and Artificial Intelligence—An Empirical Study with Partial Least Squares Structural Equation Modeling and Multi-Group Analysis
by Yi Yang and Min-Yong Kim
Sustainability 2025, 17(1), 125; https://doi.org/10.3390/su17010125 - 27 Dec 2024
Cited by 2 | Viewed by 2469
Abstract
Despite the advancement in autonomous vehicles, public trust and acceptance are crucial for AV’s widespread adoption. This study examines how different collaboration levels between human drivers and artificial intelligence influence users’ trust and acceptance of AVs. Using an extended Technology Acceptance Model, this [...] Read more.
Despite the advancement in autonomous vehicles, public trust and acceptance are crucial for AV’s widespread adoption. This study examines how different collaboration levels between human drivers and artificial intelligence influence users’ trust and acceptance of AVs. Using an extended Technology Acceptance Model, this study incorporates psychological factors and technological attitudes such as perceived safety, perceived risk, AI literacy, and AI technophobia. Data collected from 392 vehicle owners across 11 Chinese cities were analyzed using Partial Least Squares Structural Equation Modeling and Multi-Group Analysis. The findings reveal that at the fully manual level, perceived ease of use significantly influences perceived usefulness, while trust remains grounded in mechanical reliability rather than AI systems. In contrast, as AI assumes driving responsibilities at collaborative automation levels, the findings show that AI literacy significantly increases perceived trust and ease of use, while AI technophobia decreases them, with these effects varying across different driving automation levels. As AI takes on greater driving responsibilities, perceived ease of use becomes less critical, and perceived trust increasingly influences users’ acceptance. These findings highlight the need for targeted public education and phased automation strategies, offering guidance for AV developers to address user concerns and build trust in autonomous technologies. By enhancing public trust and acceptance, this study contributes to sustainable development by promoting safer roads and enabling more efficient, resource-conscious transportation systems. Gradually integrating AVs into urban mobility also supports smart city initiatives, fostering more sustainable urban environments. Full article
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