Dataset on factors influencing pedestrian crosswalk usage behavior in high-density urban areas of a developing country.

The dataset consists of survey data on pedestrian crosswalk usage behavior in high-density urban areas of a developing country, specifically collected from Dhaka, the capital city of Bangladesh. Data were gathered through a questionnaire survey conducted at twelve key locations, covering eight attributes related to crosswalk behavior and the demographic details of respondents. The survey yielded 682 valid responses, focusing on factors such as the suitability of crosswalk locations, guard rails, and lighting. The dataset is structured to support analyses using supervised machine learning techniques, facilitating reproducibility, secondary analysis, and policy development for pedestrian safety improvements. Furthermore, the dataset can be reused for cross-validation of future studies, comparison with pedestrian behavior in similar urban settings, and the development of predictive models to enhance pedestrian infrastructure in other developing regions.

A Roadside Precision Monocular Measurement Technology for Vehicle-to-Everything (V2X).

Within the context of smart transportation and new infrastructure, Vehicle-to-Everything (V2X) communication has entered a new stage, introducing the concept of holographic intersection. This concept requires roadside sensors to achieve collaborative perception, collaborative decision-making, and control. To meet the high-level requirements of V2X, it is essential to obtain precise, rapid, and accurate roadside information data. This study proposes an automated vehicle distance detection and warning scheme based on camera video streams. It utilizes edge computing units for intelligent processing and employs neural network models for object recognition. Distance estimation is performed based on the principle of similar triangles, providing safety recommendations. Experimental validation shows that this scheme can achieve centimeter-level distance detection accuracy, enhancing traffic safety. This approach has the potential to become a crucial tool in the field of traffic safety, providing intersection traffic target information for intelligent connected vehicles (ICVs) and autonomous vehicles, thereby enabling V2X driving at holographic intersections.

Effect of A-Pillar Blind Spots on a Driver's Pedestrian Visibility during Vehicle Turns at an Intersection.

This study aims to elucidate the impact of A-pillar blind spots on drivers' visibility of pedestrians during left and right turns at an intersection. An experiment was conducted using a sedan and a truck, with a professional test driver participating. The driver was instructed to maintain sole focus on a designated pedestrian model from the moment it was first sighted during each drive. The experimental results revealed how the blind spots caused by A-pillars occur and clarified the relationship between the pedestrian visible trajectory distance and specific vehicle windows. The results indicated that the shortest trajectory distance over which a pedestrian remained visible in the sedan was 17.6 m for a far-side pedestrian model during a right turn, where visibility was exclusively through the windshield. For the truck, this distance was 20.9 m for a near-side pedestrian model during a left turn, with visibility through the windshield of 9.5 m (45.5% of 20.9 m) and through the passenger-side window of 11.4 m (54.5% of 20.9 m). Additionally, we quantified the trajectory distances where pedestrians became invisible when the driver's view was obstructed by A-pillars. The sedan exhibited the highest invisibility rate at 46.1% for a far-side pedestrian model during a right turn, followed by the truck at 17.8% for the same model. These findings will be instrumental in developing new driving support systems aimed at enhancing visibility in situations where pedestrians are obscured by A-pillars.

How state mindfulness affects mobile phones usage while walking: A daily diary study in China.

INTRODUCTION: Pedestrians are a particularly vulnerable group of road users. Mobile phone usage while walking (MPUWW) is a significant contributor to pedestrians' involvement in road crashes and associated injuries. The current study aims to explore the effect of state mindfulness on daily MPUWW via phone dependence (at the within-person level), and the moderating role of risk perception (at the between-person level) in the phone dependence-MPUWW relationship. METHOD: We utilized a fine-grained method, the daily diary methodology (DDM) to explore the aforementioned model. A total of 88 Chinese college students participated in a consecutive 12-day study, yielding 632 daily data. Unconflated multilevel modeling was used to analyze the data. RESULTS: After trait mindfulness being controlled, state mindfulness has a negative impact on MPUWW via phone dependence at the daily level. Furthermore, risk perception as an individual difference variable moderates the relationship between phone dependence and MPUWW, in which a weaker effect observed in individuals with higher levels of risk perception. CONCLUSIONS: State mindfulness can decrease the frequency of daily MPUWW by reducing phone dependence, and risk perception is a crucial factor in mitigating the negative effects of phone dependence on MPUWW. PRACTICAL APPLICATIONS: To lower MPUWW and thereby minimize the risk of road crashes and associated injuries, it is beneficial to foster present-moment awareness of individuals, encourage individuals to use mobile phones in a balanced and sensible manner, and integrate the enhancement of risk perception into road safety education.

The Effectiveness of eHMI Displays on Pedestrian-Autonomous Vehicle Interaction in Mixed-Traffic Environments.

External human-machine interfaces (eHMIs) serve as communication bridges between autonomous vehicles (AVs) and road users, ensuring that vehicles convey information clearly to those around them. While their potential has been explored in one-to-one contexts, the effectiveness of eHMIs in complex, real-world scenarios with multiple pedestrians remains relatively unexplored. Addressing this gap, our study provides an in-depth evaluation of how various eHMI displays affect pedestrian behavior. The research aimed to identify eHMI configurations that most effectively convey an AV's information, thereby enhancing pedestrian safety. Incorporating a mixed-methods approach, our study combined controlled outdoor experiments, involving 31 participants initially and 14 in a follow-up session, supplemented by an intercept survey involving 171 additional individuals. The participants were exposed to various eHMI displays in crossing scenarios to measure their impact on pedestrian perception and crossing behavior. Our findings reveal that the integration of a flashing green LED, robotic sign, and countdown timer constitutes the most effective eHMI display. This configuration notably increased pedestrians' willingness to cross and decreased their response times, indicating a strong preference and enhanced concept understanding. These findings lay the groundwork for future developments in AV technology and traffic safety, potentially guiding policymakers and manufacturers in creating safer urban environments.

Age, sex, sensation-seeking, and road-crossing: How does risk context impact children's street-crossing?

OBJECTIVE: Child pedestrian injuries are a significant public health problem, largely because children have underdeveloped cognitive-perceptual capacity to judge traffic unsupervised. This study used a virtual reality (VR) environment to examine the impact of children's age, as well as sex and sensation-seeking personality, on pedestrian behavior in different risk contexts. METHODS: 405 Norwegian children (7-10-year-olds) engaged in street-crossing scenarios within a VR environment. Children crossed a bicycle path and urban roadway six times, each with increasing density and complexity of traffic. Hits and near hits were recorded. Self-reported sensation-seeking personality was assessed. RESULTS: Children were more likely to experience crashes in the tasks that offered higher probability risk. Overall, 106 children crossed safely in all tasks. Dangerous crossings were associated with male sex, higher thrill and intensity seeking personality, and denser traffic. Age was not related to any traffic safety outcomes. CONCLUSION: As expected, children were struck by vehicles more often in complex traffic contexts than in less complex ones. The results support previous findings and suggest that boys and sensation seekers have elevated risk of pedestrian injury, and that individual differences in children, rather than age alone, must be considered when determining if children are capable of safely negotiating traffic unsupervised.

Child pedestrian safety training in virtual reality: How quickly do children achieve adult functioning and what individual differences impact learning efficiency?

INTRODUCTION: Pedestrian injuries represent a leading cause of child death globally. One prevention strategy is teaching children street-crossing skills. Virtual reality (VR) has emerged as a strategy to offer repeated street-crossing practice and overcome ethical barriers of training children in live traffic. This study addressed two questions pertinent to implementation of child pedestrian safety training within VR: (a) how much training do children require to achieve adult street-crossing competency, and (b) what individual differences might facilitate children to acquire that competency more efficiently? METHODS: Five hundred 7- and 8-year-olds were recruited. Children completed pedestrian safety training within VR for up to 25 thirty-minute training sessions until they achieved adult levels of mastery. At baseline, four cognitive-perceptual skills (visual memory, visual perception, processing speed, working memory) and parent-reported externalizing symptomatology were assessed. RESULTS: On average, children achieved adult pedestrian safety competency after 10.0 training sessions (SD = 4.8). Just one child (<1%) failed to achieve adult pedestrian functioning after 25 training sessions. In univariate analyses, boys took slightly longer than girls to achieve adult functioning, and visual memory, visual perception, processing speed, working memory, and fewer externalizing symptoms were all positively associated with shorter time to mastery. In a multivariable model, only child age was a statistically significant predictor. CONCLUSION: Almost all participants achieved adult street-crossing skills competency through VR training, although they required about 10 sessions on average. Analysis of predictor variables confirmed that nearly all 7- and 8-year-olds are trainable. PRACTICAL APPLICATION: Implementation of VR pedestrian safety training is recommended, but must be conducted cautiously to ensure children are not permitted to engage independently in traffic until they are assessed and demonstrate sufficient skills.

Effects of driver's braking behavior by the real-time pedestrian scale warning system.

A driver warning system can improve pedestrian safety by providing drivers with alerts about potential hazards. Most driver warning systems have primarily focused on detecting the presence of pedestrians, without considering other factors, such as the pedestrian's gender and speed, and whether pedestrians are carrying luggage, that can affect driver braking behavior. Therefore, this study aims to investigate how driver braking behavior changes based on the information about the number of pedestrians in a crowd and examine if a developed warning system based on this information can induce safe braking behavior. For this purpose, an experiment scenario was conducted using a virtual reality-based driving simulator and an eye tracker. The collected driver data were analyzed using mixed ANOVA to derive meaningful conclusions. The research findings indicate that providing information about the number of pedestrians in a crowd has a positive impact on driver braking behavior, including deceleration, yielding intention, and attention. Particularly, It was found that in scenarios with a larger number of pedestrians, the Time to Collision (TTC) and distance to the crosswalk were increased by 12%, and the pupil diameter was increased by 9%. This research also verified the applicability of the proposed warning system in complex road environments, especially under conditions with poor visibility such as nighttime. The system was able to induce safe braking behavior even at night and exhibited consistent performance regardless of gender. In conclusion, considering various factors that influence driver behavior, this research provides a comprehensive understanding of the potential and effectiveness of a driver warning system based on information about the number of pedestrians in a crowd in complex road environments.

How does street environment affect pedestrian crash risks? A link-level analysis using street view image-based pedestrian exposure measurement.

Street space plays a critical role in pedestrian safety, but the influence of fine-scale street environment features has not been sufficiently understood. To analyze the effect of the street environment at the link level, it is essential to account for the spatial variation of pedestrian exposure across street links, which is challenging due to the lack of detailed pedestrian flow data. To address these issues, this study proposes to extract link-level pedestrian exposure from spatially ubiquitous street view images (SVIs) and investigate the impact of fine-scale street environment on pedestrian crash risks, with a particular focus on pedestrian facilities (e.g., crossing and sidewalk design). Both crash frequency and severity are analyzed at the link level, with the latter incorporating two distinct aggregation metrics: maximum severity and medium severity. Using Hong Kong as a case study, the results show that the link-level pedestrian exposure extracted from SVIs can lead to better model fit than alternative zone-level measurements. Specifically, higher pedestrian exposure is found to increase the total pedestrian crash frequency, while reducing the risk of serious injuries or fatalities, confirming the "safety in numbers" effect for pedestrians. Pedestrian facilities are also shown to influence pedestrian crash frequency and severity in different ways. The presence of crosswalks can increase crash frequency, but denser crosswalk design mitigates this effect. In addition, two-side sidewalks can increase crash frequency, while the absence of sidewalks leads to higher risks of crash severity. These findings highlight the importance of fine-scale street environment and pedestrian facility design for pedestrian safety.

Pedestrian crash risk analysis using extreme value models: New insights and evidence.

Facilitating proactive pedestrian safety management, the application of extreme value theory (EVT) models has gained popularity due to its extrapolation capabilities of estimating crashes from their precursors (i.e., conflicts). However, past studies either applied EVT models for crash risk analysis of autonomous vehicle-pedestrian interactions or human-driven vehicle-pedestrian interactions at signalised intersections. However, our understanding of human-driven vehicle-pedestrian interactions remains elusive because of scant evidence of (i) EVT models' application for heterogeneous traffic conditions, (ii) appropriate set of determinants, (iii) which EVT approach to be used, and (iv) which conflict measure is appropriate. Addressing these issues, the objective of this study is to investigate pedestrian crash risk analysis in heterogeneous and disordered traffic conditions, where drivers do not follow lane disciplines. Eleven-hour video recording was collected from a busy pedestrian crossing at a midblock location in India and processed using artificial intelligence techniques. Vehicle-pedestrian interactions are characterised by two conflict measures (i.e., post encroachment time and gap time) and modelled using block maxima and peak over threshold approaches. To handle the non-stationarity of pedestrian conflict extremes, several explanatory variables are included in the models, which are estimated using the maximum likelihood estimation procedure. Modelling results indicate that the EVT models provide reasonable estimates of historical crash records at the study location. From the EVT models, a few key insights related to vehicle-pedestrian interactions are as follows. Firstly, a comparison of EVT models shows that the peak over threshold model outperforms the block maxima model. Secondly, post encroachment time conflict measure is found to be appropriate for modelling vehicle-pedestrian interactions compared to gap time. Thirdly, pedestrian crash risk significantly increases when they interact with two-wheelers in contrast with interactions involving buses where the crash risk decreases. Fourthly, pedestrian crash risk decreases when they cross in groups compared to crossing individually. Finally, pedestrian crash risk is positively related to average vehicle speed, pedestrian speed, and five-minute post encroachment time counts less than 1.5 s. Further, different block sizes are tested for the block maxima model, and the five-minute block size yields the most accurate and precise pedestrian crash estimates. These findings demonstrate the applicability of extreme value analysis for heterogeneous and disordered traffic conditions, thereby facilitating proactive safety management in disordered and undisciplined lane conditions.

Impact of urban form and street infrastructure on pedestrian-motorist collisions.

This study examines the impact of urban form and street infrastructure on pedestrian safety in Atlanta, Georgia, and Boston, Massachusetts. With a significant rise in pedestrian fatalities over the past decade, understanding how cities' built environments influence safety is critical. We conducted geospatial analyses and statistical tests, revealing unique patterns in each city. Atlanta's sprawling, motorist-oriented layout is associated with increased pedestrian accidents, particularly at crosswalks, due to limited land use diversity, arterial roads, and streets with high speed limits and multiple lanes. In contrast, Boston's compact, pedestrian-oriented design leads to improved safety, featuring safer pedestrian crossings, greater land use diversity, reduced arterial roads and lower speed limits on single-lane streets. This study also highlights the importance of diverse urban forms and pedestrian infrastructure in shaping pedestrian safety. While population density and land use diversity impact accident rates, the presence of crosswalks and street configurations play crucial roles. Our findings underscore the urgency for urban planners to prioritize pedestrian safety through targeted interventions, such as enhancing crosswalks, reducing speed limits and promoting mixed land use. Future research should explore additional variables, alternative modelling techniques and non-linear approaches to gain a more comprehensive understanding of these complex relationships.

Deciding when to cross in front of an autonomous vehicle: How child and adult pedestrians respond to eHMI timing and vehicle kinematics.

How autonomous vehicles (AVs) communicate their intentions to vulnerable road users (e.g., pedestrians) is a concern given the rapid growth and adoption of this technology. At present, little is known about how children respond to external Human Machine Interface (eHMI) signals from AVs. The current study examined how adults and children respond to the combination of explicit (eHMI signals) and implicit information (vehicle deceleration) to guide their road-crossing decisions. Children (8- to 12-year-olds) and adults made decisions about when to cross in front of a driverless car in an immersive virtual environment. The car sometimes stopped, either abruptly or gradually (manipulated within subjects), to allow participants to cross. When yielding, the car communicated its intent via a dome light that changed from red to green and varied in its timing onset (manipulated between subjects): early eHMI onset, late eHMI onset, or control (no eHMI). As expected, we found that both children and adults waited longer to enter the roadway when vehicles decelerated abruptly than gradually. However, adults responded to the early eHMI signal by crossing sooner when the cars decelerated either gradually or abruptly compared to the control condition. Children were heavily influenced by the late eHMI signal, crossing later when the eHMI signal appeared late and the vehicle decelerated either gradually or abruptly compared to the control condition. Unlike adults, children in the control condition behaved similarly to children in the early eHMI condition by crossing before the yielding vehicle came to a stop. Together, these findings suggest that early eHMI onset may lead to riskier behavior (initiating crossing well before a gradually decelerating vehicle comes to a stop), whereas late eHMI onset may lead to safer behavior (waiting for the eHMI signal to appear before initiating crossing). Without an eHMI signal, children show a concerning overreliance on gradual vehicle deceleration to judge yielding intent.

The association between health literacy and pedestrian safety behavior among adults: a cross-sectional study.

INTRODUCTION: Pedestrians are considered the most vulnerable and complex road users as human behavior constitutes one of the fundamental reasons for traffic-related incidents involving pedestrians. However, the role of health literacy as a predictor of Pedestrian safety behavior remains underexplored. Therefore, the current study was designed to examine the level of health literacy and its association with the safety behavior of adult pedestrians in the city of Tabriz. METHODS: This cross-sectional analytical study was conducted among individuals aged 18 to 65 years in the metropolitan area of Tabriz from January to April 2023. Data were collected using the HELIA standard questionnaire (Health Literacy Instrument for adults), comprising 33 items across 5 domains (access, reading, understanding, appraisal, decision-making and behavior), as well as the Pedestrian Behavior Questionnaire (PBQ) consisting of 29 items. Data were analyzed using descriptive and analytical statistics (independent t-tests, ANOVA, and Pearson correlation coefficient) via SPSS-22 software. RESULTS: Based on the results, 94% (376 individuals) had excellent health literacy levels, and their safety behavior scores were at a good level. Health literacy and safety behavior were higher among the age group of 31 to 45 years, women, married individuals, those who read books, and individuals with higher education. However, safety behavior showed no significant association with education level (P > 0.05). There was a significant and positive relationship between health literacy and all its domains and pedestrian safety behavior (r = 0.369, P < 0.001). CONCLUSION: This study underscores the significant impact of health literacy on pedestrians' safety behavior. The findings reveal that higher levels of health literacy are associated with better safety behavior among individuals aged 18 to 63. Demographic factors such as age, gender, marital status, and education level also play a role in shaping both health literacy and safety behavior. By recognizing these relationships, interventions can be tailored to improve health literacy levels and promote safer pedestrian practices, ultimately contributing to a healthier and safer community in Tabriz city.

Driving Behavior during Left-Turn Maneuvers at Intersections on Left-Hand Traffic Roads.

Understanding left-turn vehicle-pedestrian accident mechanisms is critical for developing accident-prevention systems. This study aims to clarify the features of driver behavior focusing on drivers' gaze, vehicle speed, and time to collision (TTC) during left turns at intersections on left-hand traffic roads. Herein, experiments with a sedan and light-duty truck (< 7.5 tons GVW) are conducted under four conditions: no pedestrian dummy (No-P), near-side pedestrian dummy (Near-P), far-side pedestrian dummy (Far-P) and near-and-far side pedestrian dummies (NF-P). For NF-P, sedans have a significantly shorter gaze time for left-side mirrors compared with light-duty trucks. The light-duty truck's average speed at the initial line to the intersection (L1) and pedestrian crossing line (L0) is significantly lower than the sedan's under No-P, Near-P, and NF-P conditions, without any significant difference between any two conditions. The TTC for sedans is significantly shorter than that for trucks with near-side pedestrians (Near-P and NF-P) and far-side pedestrians in Far-P. These insights can contribute to the ongoing development of accident-prevention safety systems for left-turning maneuvers at intersections.

The attentional guidance and facilitating effects of group behavioral cues on individual college pedestrians' jaywalking decisions.

OBJECTIVE: Jaywalking is an important cause of pedestrian-related automobile accidents. Exploring the factors that influence jaywalking behavior and suggesting appropriate improvement measures are critical for reducing automobile accidents involving pedestrians. METHODS: This study divided traffic situations into high-risk and low-risk situations. Each situation contained three visual attention cues: vehicle, traffic light, and group behavior. Based on this, the role of visual cues in guiding pedestrians' attention and influencing their decisions during jaywalking was examined. Sixty participants, with an average age of 19, were recruited. They were shown 84 crosswalk videos randomly while their crossing decisions and eye movement data were recorded. RESULTS: In low-risk situations, pedestrians spent more attention on group behavioral cues when making jaywalking decisions. The rate of jaywalking increased with the number of other jaywalking pedestrians. In high-risk situations, the pedestrians' total fixation duration at vehicle hazard cues was longer when making jaywalking decisions, and the jaywalking rate decreased. CONCLUSIONS: The results indicate that pedestrians' jaywalking decisions were based on other pedestrians' illegal crossing cues and automatic associative processes in low-risk situations. The higher the number of people crossing the street, the higher the number of pedestrians illegally crossing the road. In high-risk situations, pedestrians paid more attention to vehicle hazard cues before making jaywalking decisions, and fewer illegal crossings. The jaywalking decisions were based on a risk assessment, a controlled analytical process. The results verify the effect of visual cues on pedestrians' attentional guidance and decision-making in different traffic situations, as well as the effectiveness of visual attention in predicting decision intention. The findings provide a theoretical basis and data reference for pedestrian safety education and constructing an intelligent driving pedestrian trajectory prediction model.

Revisiting the hybrid approach of anomaly detection and extreme value theory for estimating pedestrian crashes using traffic conflicts obtained from artificial intelligence-based video analytics.

Pedestrians represent a group of vulnerable road users who are at a higher risk of sustaining severe injuries than other road users. As such, proactively assessing pedestrian crash risks is of paramount importance. Recently, extreme value theory models have been employed for proactively assessing crash risks from traffic conflicts, whereby the underpinning of these models are two sampling approaches, namely block maxima and peak over threshold. Earlier studies reported poor accuracy and large uncertainty of these models, which has been largely attributed to limited sample size. Another fundamental reason for such poor performance could be the improper selection of traffic conflict extremes due to the lack of an efficient sampling mechanism. To test this hypothesis and demonstrate the effect of sampling technique on extreme value theory models, this study aims to develop hybrid models whereby unconventional sampling techniques were used to select the extreme vehicle-pedestrian conflicts that were then modelled using extreme value distributions to estimate the crash risk. Unconventional sampling techniques refer to unsupervised machine learning-based anomaly detection techniques. In particular, Isolation forest and minimum covariance determinant techniques were used to identify extreme vehicle-pedestrian conflicts characterised by post encroachment time as the traffic conflict measure. Video data was collected for four weekdays (6 am-6 pm) from three four-legged intersections in Brisbane, Australia and processed using artificial intelligence-based video analytics. Results indicate that mean crash estimates of hybrid models were much closer to observed crashes with narrower confidence intervals as compared with traditional extreme value models. The findings of this study demonstrate the suitability of machine learning-based anomaly detection techniques to augment the performance of existing extreme value models for estimating pedestrian crashes from traffic conflicts. These findings are envisaged to further explore the possibility of utilising more advanced machine learning models for traffic conflict techniques.

A ward level analysis of child pedestrian casualty frequencies in Greater London.

INTRODUCTION: Child pedestrian safety remains a challenge despite the remarkable progress that has been attained in recent years, particularly, in high income jurisdictions such as London. This study sought to identify and quantify the magnitude of the effects of various explanatory variables, from the domains of transport, built and natural environment, socio-demographic and economic factors, on ward level child pedestrian injury frequencies in Greater London. METHOD: We adopted a multilevel random parameters model to investigate the factors associated with child pedestrian injuries given the hierarchical nature of the data comprising of wards nested within boroughs. RESULTS: We found that crime, the Black, Asian, and Minority Ethnic (BAME) population, school enrollment, and the proportion of the population who walk five times a week had an increasing effect on the number of child pedestrian casualties. Conversely, the proportion of the population with a level 4 qualification and the number of cars per household had a decreasing effect. CONCLUSIONS: Our study identified high child pedestrian injury frequency wards and boroughs: Stratford and New Town had the highest expected child pedestrian injury frequencies followed by Selhurst, Westend, and Greenford Broadway. Some inner London boroughs are among the highest injury frequency areas; however, a higher number of high child pedestrian injury boroughs are in outer London. PRACTICAL APPLICATIONS: The paper provides recommendations for policy makers for targeted child pedestrian safety improvement interventions and prioritization to optimize the utilization of often constrained resources. The study also highlights the importance of considering social inequities in policies that aim at improving child traffic safety.

Augmented reality for supporting the interaction between pedestrians and automated vehicles: an experimental outdoor study.

Introduction: Communication from automated vehicles (AVs) to pedestrians using augmented reality (AR) could positively contribute to traffic safety. However, previous AR research for pedestrians was mainly conducted through online questionnaires or experiments in virtual environments instead of real ones. Methods: In this study, 28 participants conducted trials outdoors with an approaching AV and were supported by four different AR interfaces. The AR experience was created by having participants wear a Varjo XR-3 headset with see-through functionality, with the AV and AR elements virtually overlaid onto the real environment. The AR interfaces were vehicle-locked (Planes on vehicle), world-locked (Fixed pedestrian lights, Virtual fence), or head-locked (Pedestrian lights HUD). Participants had to hold down a button when they felt it was safe to cross, and their opinions were obtained through rating scales, interviews, and a questionnaire. Results: The results showed that participants had a subjective preference for AR interfaces over no AR interface. Furthermore, the Pedestrian lights HUD was more effective than no AR interface in a statistically significant manner, as it led to participants more frequently keeping the button pressed. The Fixed pedestrian lights scored lower than the other interfaces, presumably due to low saliency and the fact that participants had to visually identify both this AR interface and the AV. Discussion: In conclusion, while users favour AR in AV-pedestrian interactions over no AR, its effectiveness depends on design factors like location, visibility, and visual attention demands. In conclusion, this work provides important insights into the use of AR outdoors. The findings illustrate that, in these circumstances, a clear and easily interpretable AR interface is of key importance.

Do Simulated Augmented Reality Overlays Influence Street-Crossing Decisions for Non-Mobility-Impaired Older and Younger Adult Pedestrians?

OBJECTIVE: This study used a virtual environment to examine how older and younger pedestrians responded to simulated augmented reality (AR) overlays that indicated the crossability of gaps in a continuous stream of traffic. BACKGROUND: Older adults represent a vulnerable group of pedestrians. AR has the potential to make the task of street-crossing safer and easier for older adults. METHOD: We used an immersive virtual environment to conduct a study with age group and condition as between-subjects factors. In the control condition, older and younger participants crossed a continuous stream of traffic without simulated AR overlays. In the AR condition, older and younger participants crossed with simulated AR overlays signaling whether gaps between vehicles were safe or unsafe to cross. Participants were subsequently interviewed about their experience. RESULTS: We found that participants were more selective in their crossing decisions and took safer gaps in the AR condition as compared to the control condition. Older adult participants also reported reduced mental and physical demand in the AR condition compared to the control condition. CONCLUSION: AR overlays that display the crossability of gaps between vehicles have the potential to make street-crossing safer and easier for older adults. Additional research is needed in more complex real-world scenarios to further examine how AR overlays impact pedestrian behavior. APPLICATION: With rapid advances in autonomous vehicle and vehicle-to-pedestrian communication technologies, it is critical to study how pedestrians can be better supported. Our research provides key insights for ways to improve pedestrian safety applications using emerging technologies like AR.

Road Users Fail to Appreciate the Special Optical Properties of Retroreflective Materials.

OBJECTIVE: To determine whether typical road users appreciate the special optical properties of retroreflective materials. BACKGROUND: Retroreflective surfaces reflect light back towards the source of the illumination. All drivers benefit from retroreflective materials, as they are required on road signs, on large trailers, in lane delineation, and other traffic control devices. Retroreflective markings can also greatly enhance the conspicuity of pedestrians at night, but pedestrians typically underuse retroreflective markings. One possible reason is that pedestrians may not appreciate the special optical properties of retroreflective materials. METHOD: Two experiments tested whether observers could correctly predict that retroreflective materials appear remarkably bright when illuminated by a source that is aligned with the observers' eyes. Observers used a magnitude estimation procedure to predict how bright retroreflective and non-retroreflective stimuli would appear during a demonstration designed to highlight retroreflectivity. They then judged the brightness again during the demonstration. RESULTS: In general, observers underestimated how bright retroreflective stimuli would be and overestimated how bright diffuse reflective and fluorescent stimuli would be. The underestimates for retroreflective stimuli were particularly striking when the observers had not closely examined the stimuli in advance. CONCLUSION: The fact that road users do not appreciate retroreflectivity may help explain why pedestrians underuse retroreflective markings at night. APPLICATION: Educational interventions could prove useful in this domain.