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Information deviations are inevitable under the influence of multifarious factors in real-world traffic, leading to discrepancies between the information obtained by automated vehicles (AVs) and the true information. However, due to the lack of an appropriate analytical model that incorporates various information with deviations, we have limited knowledge of the relationships between different types of information deviations and anomalous dynamics of AVs and traffic flow. This study aims to fill this gap. Specifically, we first expound the possible information deviations in AVs, upon which we categorize them into three types: velocity, gap, and driving decision deviations. Subsequently, we modify the input parameters in the adaptive cruise control (ACC) model that calibrated using real experimental data to capture the car-following dynamics of AVs with information deviations. By using H ∞ control theory and characteristic equation-based method, we derive the local and string stability criteria of traffic flow with AVs, so as to discern the effects of various system factors on traffic flow stability. Experimental results show that information deviations could provoke abrupt acceleration or deceleration of AVs, leading to instability in automated traffic flow, oscillation, and even collision accidents. Overall, this paper unveils the influence mechanisms of diverse information deviations on AVs and traffic flow, providing valuable suggestions and theoretical guidance for the future development of AVs. [ABSTRACT FROM AUTHOR]

Vehicles planning to exit at an upcoming freeway off-ramp require adequate exit distance to execute lane change maneuvers, otherwise, traffic disturbances may be experienced. This research assesses the changes in exit distances for a mixed traffic environment comprising automated vehicles (AVs) and human-driven vehicles (HDVs) on freeways with and without a managed lane (ML). A left-side continuous ML was designed and eligible vehicles were AVs. Traffic microsimulation exercises were conducted on a 3.5 km freeway segment, and scenarios varied based on traffic demand, the number of freeway lanes, and AV adoption rates. Traffic demand was set relative to the queue discharge flow rate (qQ). The results indicated an increase in qQ as the AV adoption rate increased. Also, the exit distances were influenced by the traffic environment, the freeway configuration, and the traffic demand. The optimal exit distance increased when an ML was implemented at 25% and 50% AV adoption rates. [ABSTRACT FROM AUTHOR]

Level 3 automated vehicles (L3 AVs) enable the driver to perform non-driving tasks, taking over in an emergency. In recent years, studies have extensively discussed the influencing factors of L3 AV takeovers. Extensive literature review shows that L3 AV takeovers are affected by human factors, traffic environment, and automatic driving systems. On this basis, this study proposes a conceptual framework of L3 AV takeovers. The main findings of this study include the following: (1) non-driving tasks, non-driving posture, individual characteristics, and trust have an impact on takeover behavior; (2) high traffic density, poor road geometry, and extreme weather have a negative impact on the takeover; (3) multimodal interaction design can improve collection performance. Although the existing research has made rich achievements, there are still many challenges. The influence of human factors on takeover performance is controversial, the quantification standard of takeover influencing factors is insufficient, and the prediction accuracy needs to be improved. It is suggested to refine the criteria of driver participation in NDRT, formulate an effective measurement standard of driver fatigue, and develop a takeover prediction model combining driver status and traffic environment conditions. It provides a research basis for the formulation of laws, infrastructure construction, and human–computer interaction design. [ABSTRACT FROM AUTHOR]

• A cycling simulator was used to recreate the cyclists' interaction scenarios with motorized vehicles. • The effect of the difference in time to arrival at the intersection and visibility distance was observed on the interaction. • Cyclists' behavioral patterns were investigated in response to independent variables. • Eye contact and communication with the driver plays an important role in cyclists decision-making. Introduction: With cycling gaining more popularity in urban areas, it is vital to obtain accurate knowledge of cyclists' behavior to develop behavioral models that can predict the cyclist's intent. Most conflicts between cyclists and vehicles happen at crossings where the road users share the path, especially at unsignalized intersections. However, few studies have investigated and modeled the interaction between cyclists and vehicles at unsignalized intersections. Method: A bike simulator experiment was conducted to scrutinize cyclists' response process as they interacted with a passenger car at an unsignalized intersection. An existing unsignalized intersection in Gothenburg was simulated for test participants. Two independent variables were varied across trials: the difference in time to arrival at the intersection (DTA) and intersection visibility (IV). Subjective and quantitative data were analyzed to model the cyclists' behavior. Results: When approaching the intersection, cyclists showed a clear sequence of actions (pedaling, braking, and head turning). The distance from the intersection at which cyclists started braking was significantly affected by the two independent variables. It was also found that DTA, looking duration, and pedaling behavior significantly affected cyclists' decisions to yield. Finally, the questionnaire outputs show that participants missed eye contact or communication with the motorized vehicle. Conclusions: The kinematic interaction between cyclists and vehicles, along with the cyclist's response process (visual and kinematic), can be utilized to predict cyclists' yielding decision at intersections. From the infrastructural perspective, enhancing visibility at intersections has the potential to reduce the severity of interactions between cyclists and vehicles. The analysis of the questionnaire emphasizes the significance of visual communication between cyclists and drivers to support the cyclist's decision-making process when yielding. Practical applications: The models can be used in threat assessment algorithms so that active safety systems and automated vehicles can react safely to the presence of cyclists in conflict scenarios. [ABSTRACT FROM AUTHOR]

Autonomous vehicles have limited computational resources and thus require efficient control systems. The cost and size of sensors have limited the development of self-driving cars. To overcome these restrictions, this study proposes an efficient framework for the operation of vision-based automatic vehicles; the framework requires only a monocular camera and a few inexpensive radars. The proposed algorithm comprises a multi-task UNet (MTUNet) network for extracting image features and constrained iterative linear quadratic regulator (CILQR) and vision predictive control (VPC) modules for rapid motion planning and control. MTUNet is designed to simultaneously solve lane line segmentation, the ego vehicle's heading angle regression, road type classification, and traffic object detection tasks at approximately 40 FPS for 228 × 228 pixel RGB input images. The CILQR controllers then use the MTUNet outputs and radar data as inputs to produce driving commands for lateral and longitudinal vehicle guidance within only 1 ms. In particular, the VPC algorithm is included to reduce steering command latency to below actuator latency, preventing performance degradation during tight turns. The VPC algorithm uses road curvature data from MTUNet to estimate the appropriate correction for the current steering angle at a look-ahead point to adjust the turning amount. The inclusion of the VPC algorithm in a VPC-CILQR controller leads to higher performance on curvy roads than the use of CILQR alone. Our experiments demonstrate that the proposed autonomous driving system, which does not require high-definition maps, can be applied in current autonomous vehicles. [ABSTRACT FROM AUTHOR]

Liability attribution for crashes involving automated vehicles (AVs), if applied improperly, is a factor which can potentially hinder acceptance. The present study investigated the impact of liability attribution on intention to buy an AV. A vignette-based survey was implemented with a hypothetical crash similar to the 2018 Uber crash (which was jointly caused by driver distraction and the malfunctioning of the automated system) leading to a pedestrian's fatality. Respondents (N = 1524) chose their preferred liability attribution, ranging from human driver exclusively liable to AV manufacturer exclusively liable. Respondents were then randomly allocated to different conditions of actual liability attribution by the local authority. These conditions were then combined into, negative misattribution (the authority assigned more liability to the human driver, compared to the respondent), positive misattribution (the authority assigned less liability to the human driver), and no misattribution. Negative misattribution negatively affected intention to buy; however, positive misattribution did not have a significant impact. The results of a multiple-mediator model indicated that negative misattribution affects intention to buy through the mediating effects of trust, negative affect, and crash acceptability. Theoretical and practical implications of our results are discussed. [ABSTRACT FROM AUTHOR]

Introduction: Augmented reality (AR) has been increasingly studied in transportation, particularly for drivers and pedestrians interacting with automated vehicles (AVs). Previous research evaluated AR interfaces using online video-based questionnaires but lacked human-subject research in immersive environments. This study examined if prior online evaluations of nine AR interfaces could be replicated in an immersive virtual environment and if AR interface effectiveness depends on pedestrian attention allocation. Methods: Thirty participants completed 120 trials in a CAVE-based simulator with yielding and non-yielding AVs, rating the interface's intuitiveness and crossing the road when they felt safe. To emulate visual distraction, participants had to look into an attention-attractor circle that disappeared 1 s after the interface appeared. Results: The results showed that intuitiveness ratings from the current CAVEbased study and the previous online study correlated strongly (r = 0.90). Headlocked interfaces and familiar designs (augmented traffic lights, zebra crossing) yielded higher intuitiveness ratings and quicker crossing initiations than vehiclelocked interfaces. Vehicle-locked interfaces were less effective when the attention-attractor was on the environment's opposite side, while headlocked interfaces were relatively unaffected by attention-attractor position. Discussion: In conclusion, this 'AR in VR' study shows strong congruence between intuitiveness ratings in a CAVE-based study and online research, and demonstrates the importance of interface placement in relation to user gaze direction. [ABSTRACT FROM AUTHOR]

• Drivers have more positive opinions of driver monitoring than automated driving. • Perceived reliability, security and privacy are concerns held about monitoring. • Drivers are sceptical of the value of both driver monitoring and automated driving. • The individual and societal benefits are contingent on an adequate level of trust. The ability to measure psychological states such as fatigue will become increasingly important with the introduction of automated driving systems (ADS) to everyday driving. Driver monitoring systems (DMS), which will soon be a required feature in all new vehicles, will be responsible for assessing the driver's mental state in real-time. This will help to maximise the safety and social benefits of ADS. However, little is known about drivers' perceptions of DMS. This qualitative focus group study used a reflexive thematic analysis approach to understand drivers' perspectives on the use of DMS during automated driving. Seventeen drivers with no prior experience of ADS or DMS were interviewed across three focus group sessions and were shown a video outlining some of the capabilities of both systems. A semi-structured interview guide was used to gather qualitative data concerning drivers' perceptions of the prospect of driver monitoring within automated driving and the expectations that they have. Reflexive thematic analysis was used to develop five themes. The findings show that drivers have more favourable attitudes towards DMS than ADS, due to an expectation that the latter will require a greater sacrifice of the driver's control. Nonetheless, participants were sceptical of the reliability, security and privacy of driver monitoring, and expressed that it could detract from the enjoyment derived from driving. Participants were also concerned regarding the potential for driver data to be sold to third parties and used against them in various ways. Overall, drivers are sceptical of the value of driver monitoring and ADS and perceive them as separate entities as opposed to two systems working in partnership. This highlights an emerging challenge for researchers and system manufacturers, which will need be addressed in order to fully realise the individual and societal benefits of these new forms of technology. [ABSTRACT FROM AUTHOR]

• Assess the properties of the Pedestrian Receptivity Questionnaire for FAVs (PRQF) on a French sample. • The results showed different dimensions than those found in other validation studies. • The results showed mixed receptivity to FAVs among French respondents. • Receptivity to FAVs and self-declared pedestrian behaviors explain behavioral intention and acceptance of FAVs. • Age, familiarity with FAVs, and with new technologies also explain acceptance of FAVs. One of the biggest challenges facing the human-centered design of fully automated vehicles (FAVs) is their interaction with vulnerable road users. Measuring pedestrians' perceptions is particularly important to know whether they will be receptive to the introduction of FAVs on the road. In this context, the present study aimed to examine and validate the structure of the Pedestrian Receptivity Questionnaire for FAVs (PRQF) in France. A second objective was to explore the receptivity of French pedestrians towards FAVs, its effects on behavioral intention when interacting with FAVs, and acceptance of FAVs. To meet these objectives, 474 participants living in France answered an online survey (age range: 18–83 years; 39 % of males), which included the PRQF, the behavioral intention to cross the road in front of a FAV and their acceptance, as pedestrians, of the introduction of FAVs on the road. Several scales and items were also included to measure familiarity with FAVs and new technologies, as well as self-reported pedestrian behavior using the pedestrian behavior scale (PBS) and sociodemographic information. A series of statistical analyses indicated a three-dimensional factor structure in the answers of the French respondents to the PRQF, i.e., Positive Attitude, Supportive Social Norms, and Compatibility. These dimensions differed from those revealed in earlier questionnaire validation studies. However, in line with other studies on automated vehicle acceptance, these dimensions were influenced by several sociodemographic factors (age, sex, level of education, walking frequency, location, possession and duration of driving license). The results also indicated a mixed receptivity of the French respondents to FAVs. Their behavioral intentions to cross the road in front of an approaching FAV were further explained by the Positive Attitude dimension of the PRQF and their self-declared behaviors as pedestrians, particularly in terms of positive and violation behaviors. Acceptance of FAVs in the respondents' living area was predicted by the Positive Attitude dimension and by self-declared behaviors as pedestrians (in terms of aggressive behaviors particularly). The respondents' age and familiarity with FAVs and new technologies were also predictive of their FAV acceptance. Several research and practical perspectives are proposed accordingly. [ABSTRACT FROM AUTHOR]

The progress of technology in our current world continues to advance each day, benefiting human beings in various ways. One significant development in recent time is the emergency of automated vehicles, which have the potential to revolutionise transportation. These vehicles utilise electric power, sensors, cameras and sound navigators to carry out their intended operations without causing environmental pollution. Currently, there are several autonomous companies, primarily located in California, cities like San Francisco (Cruise), Palo Alto (Tesla), Fremont (Pony.ai), Santa Monica (Motional), Mountain View (Waymo), and Foster city (Zoox). This paper aims to review the utilisation level and performance of autonomous vehicles, specially focusing on the goals set for 2023. By analysing various research studies and company profiles, this paper aims to provide insights into the current status of autonomous vehicles and their practical applications. It employs quantitative and statistical methods to extract valuable information from these studies. Also, this paper examines the state of the art in autonomous vehicles and the impact of gaps in machine learning algorithms, from perception to execution. The data used for this study are obtained from research reviews and updated profile of different companies. The assessment reveals a significant increase in research and development activities related to autonomous and automated vehicles across various disciplines since 2010. Specifically, the number of research studies on autonomous driving vehicles has increased from 302 to 2718, while studies on automated vehicles have increased from 1379 to 6085. In the Engineering discipline alone, there have been 601 studies on autonomous driving vehicles and 341 studies on automated vehicle-related research, which have increased to 2685 and 1865, respectively in the specified time.

Driver distraction is intricately linked to human behavior and cognitive ergonomics, as it explores how human engagement with various stimuli influences attention and decision‐making processes while driving. The main purpose of this study is to comprehensively explore whether using Human–Machine Interface infotainment systems in automated vehicles can affect driver distraction. To this end, driver distraction was measured by driving performance features (speed, lane position, and reaction time), behavioral features (fixation time and pupil dilation), physiological features (changes in oxyhemoglobin), and subjective assessment (NASA‐TLX workload). Twenty‐one participants equipped with an eye tracker and functional near‐infrared spectroscopy drove a driving simulator in the current investigation. The results revealed that interacting with the infotainment systems significantly affects the drivers' average speed (F2,40 = 13.60, p <.0001), reaction time (F2,40 = 4.74, p =.0142), fixation time (F2,40 = 88.61, p <.0001), pupil dilation (F2,28 = 3.63, p =.0356), and workload (F2,40 = 14.40, p <.0001). Moreover, driving mode significantly affects drivers' speed deviation (F2,40 = 6.12, p =.0048), standard deviation of lane position (F2,40 = 10.57, p =.0002), fixation time (F2,40 = 36.71, p <.0001), and workload (F2,40 = 28.08, p <.0001). Drawing from the findings of this article and emphasizing human‐centric design principles, researchers and engineers can craft automotive technologies that are intuitive, effective, and safer. This is vital for mitigating driver distraction and guaranteeing the beneficial influence of automated vehicles on both road safety and the overall driving experience. [ABSTRACT FROM AUTHOR]

This article presents a methodology to estimate the entry capacity (EC) and total capacity (TC) of basic turbo roundabouts under partial and fully connected and autonomous vehicle (CAV) environments. EC calculations are partially based on capacity models and adjustment factors proposed by the HCM 7th edition, taking into account different proportions of CAVs in traffic streams. The proposed methodology was applied to a case study concerning a basic turbo roundabout with different traffic demands and market penetration levels (MPLs) of CAVs. It was assumed that the traffic stream consisted of 100% passenger cars with MPLs of CAVs ranging from 0% to 100%. The research proves that with the increase in MPLs of CAVs, ECs increase accordingly and delays and queues decrease. To maximize the TC, a control area was also hypothesized, where CAVs start to communicate with a turbo roundabout manager system. The system should be able to distribute and channel CAVs, and therefore the entering flows between entry lanes find the values of the maneuver distribution factors (α, β, γ, δ) between the right lane and the left lane of entries to maximize the TC for each origin–destination matrix of traffic flows.

The emergence of autonomous vehicles (AVs) is expected to significantly reshape urban mobility and travel behavior patterns. AVs have the potential to offer higher levels of convenience, safety, and accessibility while enabling users to spend their trip time on more efficient and productive tasks such as working or even relaxing. This transformation in the short- to mid-term could result in changes in the public's sensitivity and perception toward enduring longer travel times and consequently, in mid- to long-term, it could influence the willingness to reconsider their residential locations. Therefore, the objective of this study is to enhance the understanding of the potential effects of AVs on travel behavior and land use through the examination of stated preference queries. To achieve this objective, various multinomial logit models toward AVs adoption and residential relocation were estimated by employing a dataset from Istanbul, Türkiye, as an example of megacity in a developing country. While the study findings revealed a set of potential adoption barriers for AVs, they also indicated a notable propensity for adopting these vehicles. Furthermore, concerning individuals' willingness to reconsider their residential locations due to the emergence of AVs, a considerable positive attitude was evident, albeit guarded. This attitude suggests that individuals' decisions are still evolving and can be guided toward the desired future through well-timed and well-suited policies. The outcomes of the study can serve as valuable input for policymakers as well as transportation and urban planners, offering insights into the potential impacts of AVs on urban mobility and form. [ABSTRACT FROM AUTHOR]

Vehicle‐based mobile observations are taken across the world every day by operational and research meteorological organizations, public transportation agencies, and private car manufacturers. Whether directly weather‐related (e.g., air temperature) or not (e.g., wiper speed), the coverage and frequency of these observations holds the promise of filling in gaps between fixed observing stations and greatly improving situational awareness and weather forecasting, from road surface condition‐specific applications and winter road maintenance to urban and street‐level numerical weather prediction and beyond. However, in order to take advantage of these observations, the weather, water, and climate enterprise must work together with the transportation enterprise across academic, public, and private sectors to provide a mechanism for obtaining these data, so that the benefits of using these unconventional observations may be realized. [ABSTRACT FROM AUTHOR]

User acceptance is essential for successfully introducing automated vehicles (AVs). Understanding the technology is necessary to overcome skepticism and achieve acceptance. This could be achieved by visualizing (uncertainties of) AV's internal processes, including situation perception, prediction, and trajectory planning. At the same time, relevant scenarios for communicating the functionalities are unclear. Therefore, we developed EduLicit to concurrently elicit relevant scenarios and evaluate the effects of visualizing AV's internal processes. A website capable of showing annotated videos enabled this methodology. With it, we replicated the results of a previous online study (N=76) using pre-recorded real-world videos. Additionally, in a second online study (N=22), participants uploaded scenarios they deemed challenging for AVs using our website. Most scenarios included large intersections and/or multiple vulnerable road users. Our work helps assess scenarios perceived as challenging for AVs by the public and, simultaneously, can help educate the public about visualizations of the functionalities of current AVs. • EduLicit –a method to elicit user-chosen driving scenarios and educate on automated vehicle functionalities and challenges. • Applying neural networks to scenario videos to visualize vehicles' detection, prediction, and trajectory planning functionalities. • Website implementation of EduLicit , where users upload in-the-wild driving videos for automated visualization and education. • User-chosen driving scenarios primarily include large intersections and/or multiple vulnerable road users. • Users perceive vulnerable road users as more unreliable, unpredictable, and harder to detect by sensors than vehicles. [ABSTRACT FROM AUTHOR]

• Attitudes of different VRU Groups towards AVs in urban traffic were studied. • Interaction with AVs and AVs in mixed traffic with CVs was explored. • Some VRUs are concerned that mixed traffic could lead to more aggression from human drivers and make traffic participation more complex. • VRUs share similar evaluations, intentions, and AV requirements in urban traffic. • VRUs want AV identification, explicit communication, and strict traffic rules. In today's urban traffic, vulnerable road users (VRUs) have a somewhat ambivalent relationship with human drivers. The introduction of automated vehicles (AVs) that are no longer controlled by such human drivers could impact this relationship, both in positive and negative ways. Five focus groups (pedestrians, cyclists, e-scooter riders, individuals with walking disabilities, and older adults) were conducted to investigate how different VRUs evaluate the participation of AVs in urban traffic and whether they hold specific requirements or concerns. The discussions focused on exploring potential interactions with AVs and the effects of mixed traffic scenarios (encompassing AVs and conventional vehicles (CVs)) on the well-being of VRUs in urban traffic. The results revealed that the concerns and expectations about AVs in urban traffic were quite similar among the various VRU groups. Regarding their interaction with AVs, participants expressed advantages (e.g., eliminating human errors) and disadvantages (e.g., lack of communication possibilities). In the context of mixed traffic, especially cyclists and individuals with walking disabilities feared aggressive behavior from CVs taking advantage of rule-abiding AVs. They also saw the risk of increasing complexity in mixed urban traffic, as AVs are likely to behave differently from CVs. In addition to the increased caution described by most VRUs, a few participants could imagine taking advantage of the defensive driving style of AVs. The cyclist group discussed the possibility of sabotaging AVs to make a general statement against cars in urban traffic. Furthermore, essential requirements were gathered for vehicle manufacturers (e.g., external Human-Machine Interfaces) and policies (e.g., stringent regulation enforcement for car traffic in the city) that could enable successful coexistence between VRUs and AVs in urban traffic. [ABSTRACT FROM AUTHOR]

Human drivers and machine drivers (i.e., automated vehicles or AVs) will share roads and interact with each other, creating mixed traffic. In this perspective, we develop two mental models about them and their social interactions, aiming to understand the risk implications of AVs and mixed traffic. Based on Mental Model I (i.e., machine drivers are superior drivers without human weaknesses), many simulation‐based safety assessments, which often overlook or oversimplify human‐AV social interactions, have predicted significant safety benefits when machine drivers interact with or replace human drivers. In contrast, Mental Model II considers human and machine drivers as heterogeneous and incompatible, suggesting that their interactions may lead to unexpected and occasionally negative outcomes, particularly in imminent mixed traffic. This perspective gains support from recent comparative empirical studies that employ various methods such as survey experiments, driving simulators, test‐tracks, on‐road observations, and AV accident analysis. These studies provide initial evidence of emerging traffic risks arising from human‐AV social interactions, including human drivers' aggression and road rage toward AVs, human drivers exploiting AVs, AVs exerting negative peer influences on human drivers, and their incompatibility increasing human drivers' challenges in joining mixed traffic and thus risky behaviors. We propose specific suggestions to mitigate problematic human‐AV social interactions and the associated emerging risks. [ABSTRACT FROM AUTHOR]

Prior research on Automated Vehicle (AV) transportation services has largely concentrated on specific cases, leading to a narrow comprehension of wider public mobility requirements in AV deployment. Additionally, the impacts of socio-demographics on AV transportation preferences remain underexplored, hindered by limitations in current modeling methodologies. Moreover, mediating roles of safety between socio-demographics and AV transportation preferences lack quantitative validation. This study aims to uncover the reasons behind public preference for AV-based services and investigate safety’s mediating effects on these preferences. In a survey conducted in Kentucky, USA, with 673 responses, the most preferred uses for AV services were post-alcohol rides, airport shuttles, and long-distance travel. Urban areas showed a higher preference for AV services. Age, gender, urbanization, affordability, travel needs, and exposure to AV technology significantly influenced these preferences. The study underscores a high demand for AV services while validating safety concerns as a major barrier to their widespread adoption. [ABSTRACT FROM AUTHOR]

The implementation of the research results is seen as a crucial step in the development of innovation in the transport sector. Moving to such an implementation is not always easy or straightforward. It requires a suitable organizational framework both inside as well as outside research producing entities and a number of other facilitating factors that are usually found within an innovation ecosystem. The paper examines systematically the conditions and prevailing practices for transport research implementation in Europe (the European Union) and China and draws useful insights as to the factors that influence such implementation, the incentives, and other facilitating provisions that the research funding organizations can take. It also analyses the current practice and lessons learned for research implementation on the road to innovation production in four major areas of transport research namely: Automated Mobility, Intelligent Railways, Shared and Micromobility applications, and Electromobility. [ABSTRACT FROM AUTHOR]

• It is difficult for drivers to understand the capability of automated driving functions and features depending on the level, leading to misuse of these functions and dangerous driving behaviors. • In this study, we developed an automated driving behavior questionnaire (ADBQ) based on information processing stages of the driving activity. • Empirical analysis showed the reliability, validty, and model fit. Further, relations between participants average driving distance, technology adoption style, and trust in automated vehicles with factors of the questionnaire. The Driver Behavior Questionnaire (DBQ), which is rooted in Human Factors, is a common tool used in the context of driving behavior. However, it does not consider driver behavior when automated-driving systems are active. The primary objective of this study is to construct a questionnaire with high levels of reliability to assess the driving behavior of automated-vehicle (AV) users. This paper presents the development and validation process of a 16-item automated driving behavior questionnaire, composed of three factors of the driving process (perception, cognition, and action) and two factors of automated driving systems (user literacy and dependency). Responses from 441 active AV users were collected and analyzed. The application of factor analysis resulted in the identification of a five-factor solution, and the results showed that male drivers exhibited higher levels of literacy, action, and reliance on AVs than female drivers. In terms of trust, those with complete trust reported higher AV dependency, whereas those with low trust reported lower AV dependency and higher cognitive scores. Addressing gender- and trust-based disparities is crucial for traffic safety, especially among male and less-trusting drivers. The developed ADBQ will serve as a supporting tool for system developers and researchers to assess the driving behaviors of AV users. [ABSTRACT FROM AUTHOR]

• The study investigates the effects of user interface on trust, perceived risk, and acceptance in partially automated driving using a driving simulator. • The study designs and evaluates four UIs combining surrounding and manoeuvre information with visual and auditory modalities. • The user interface, delivering both surrounding and manoeuvre information via visual and auditory modalities, showed the highest trust and acceptance and the lowest perceived risk. • Criticality of event types and individual differences of participants were found to have a more substantial influence on drivers' behaviour, trust, and perceived risk compared to the user interfaces. • Eye-tracking results demonstrated that drivers checked the centre-console user interface when present, with no difference between the four user interfaces. Trust and perceived safety are pivotal in the acceptance of automated vehicles and can be enhanced by providing users with automation information on the (safe) operation of the vehicle. This study aims to identify how user interfaces (UI) can enhance drivers' trust and acceptance and reduce perceived risk in partially automated vehicles. Four interfaces were designed with different levels of complexity. These levels were achieved by combining automation information (surrounding information vs surrounding and manoeuvre information) and modality (visual vs visual and auditory). These interfaces were evaluated in a driving simulator in which a partially automated vehicle reacted to an event of a merging and braking vehicle in its front. The criticality of the events was manipulated by the factors merging gap (in meters) and deceleration (m/s2) of the vehicle in front. The reaction of the automation was either to brake or to change lanes. The results show that an optimal combination of automation information and modality enhances drivers' trust and acceptance. More specifically, the most advanced UI, which provided surrounding and manoeuvre information via the visual and auditory modalities , was associated with the highest trust and acceptance ranking and the lowest perceived risk. Manoeuvre information delivered through the auditory modality was particularly effective in enhancing trust and acceptance. The benefits of the UIs were consistent over events. However, in the most critical events, drivers did not feel entirely safe and did not trust the automation completely. This study suggests that the design of UIs for partially automated vehicles shall include automation information via visual and auditory modalities. [ABSTRACT FROM AUTHOR]

The integration of Industry 4.0 and automated vehicles into the smart cities concept is a topical issue in the urbanization of cities and technological innovation within cities. As it is a relatively modern issue, many aspects of this field have not yet been explored; as a consequence, this paper is concerned with the search for synergies between Industry 4.0 and automated vehicles in smart city infrastructures. There is a lack of contributions in this field that summarize these synergies in a single article and address a wide range of aspects, including transport, energy, communication, and citizen participation. As the field lacks a complete and clear summary of what is already known, which would help multiple stakeholders, the authors decided to conduct this review. The article elucidates the above-stated aspects through a clear and in-depth literature review, which is complemented by specific examples from practice. Of course, the article also includes a description of the synergy potential and the impact on the inhabitants, the environment, and, last but not least, on the overall city life. The main hypothesis of this article is that the integration of Industry 4.0 technologies and automated vehicles within smart city infrastructure will result in significant improvements in transportation efficiency, resource utilization, and overall urban sustainability. The article discusses the positives and negatives of such integration, highlighting, on the one hand, the benefits in terms of reducing environmental impact and improving citizens' quality of life, but on the other hand, also highlighting the various ethical, legal, and social issues that such integrations may bring. Several methods have been used within the article, namely analysis, synthesis, comparison, and historical interpretation. The final discussion highlights the benefits, as well as the challenges, that such integration faces and must deal with if it is to be successful. It can be concluded that the synergistic potential of automated vehicles and Industry 4.0 in smart city infrastructure is enormous and that such integration offers promising solutions for enhancing transportation efficiency, energy management, and overall urban sustainability. It is also highlighted in the article that, in order to reap the benefits of such synergies, a wide-ranging collaboration of policymakers, industry stakeholders, and urban planners is needed. [ABSTRACT FROM AUTHOR]

The emergence of automated vehicles promises a revolution in urban mobility. To benefit from a new mobility system, women who have specific mobility considerations necessitate inclusion in designing automated vehicles. This study explores women’s perspectives and the potential impact of automated vehicles through focus group discussions and in-depth interviews. Results demonstrate concerns among women about safety in current mobility systems, vulnerabilities regarding personal safety, and stereotypes about female drivers. Additionally, mothers face additional challenges managing items for children and their demands during travel, and senior women consider safety issues and declining capabilities when contemplating driving cessation. Current experience with mobility is reflected in concerns and visions regarding automated vehicles. The absence of a driver is expressed as improved safety in driverless taxis, while it is perceived as a safety concern in automated public transportation. Mothers with children anticipate convenience in travel, whereas senior women expect enhanced mobility and social participation. These findings underscore the importance of safety in women’s mobility experiences and provide insights into addressing safety and interaction issues in the design of automated vehicles. Researchers, transportation authorities, and vehicle manufacturers can leverage these results to understand women’s needs better and consider them in future designs and policy developments for automated vehicles. Prioritising women’s perspectives in automated vehicle research is essential to realising the innovative potential of this technology and fostering a more inclusive and accessible future in urban mobility.