Dataset Information

External Human-Machine Interfaces on Automated Vehicles: Effects on Pedestrian Crossing Decisions.

ABSTRACT: OBJECTIVE:In this article, we investigated the effects of external human-machine interfaces (eHMIs) on pedestrians' crossing intentions. BACKGROUND:Literature suggests that the safety (i.e., not crossing when unsafe) and efficiency (i.e., crossing when safe) of pedestrians' interactions with automated vehicles could increase if automated vehicles display their intention via an eHMI. METHODS:Twenty-eight participants experienced an urban road environment from a pedestrian's perspective using a head-mounted display. The behavior of approaching vehicles (yielding, nonyielding), vehicle size (small, medium, large), eHMI type (1. baseline without eHMI, 2. front brake lights, 3. Knightrider animation, 4. smiley, 5. text [WALK]), and eHMI timing (early, intermediate, late) were varied. For yielding vehicles, the eHMI changed from a nonyielding to a yielding state, and for nonyielding vehicles, the eHMI remained in its nonyielding state. Participants continuously indicated whether they felt safe to cross using a handheld button, and "feel-safe" percentages were calculated. RESULTS:For yielding vehicles, the feel-safe percentages were higher for the front brake lights, Knightrider, smiley, and text, as compared with baseline. For nonyielding vehicles, the feel-safe percentages were equivalent regardless of the presence or type of eHMI, but larger vehicles yielded lower feel-safe percentages. The Text eHMI appeared to require no learning, contrary to the three other eHMIs. CONCLUSION:An eHMI increases the efficiency of pedestrian-AV interactions, and a textual display is regarded as the least ambiguous. APPLICATION:This research supports the development of automated vehicles that communicate with other road users.

SUBMITTER: de Clercq K

PROVIDER: S-EPMC6820125 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

External Human-Machine Interfaces on Automated Vehicles: Effects on Pedestrian Crossing Decisions.

de Clercq Koen K Dietrich Andre A Núñez Velasco Juan Pablo JP de Winter Joost J Happee Riender R

Human factors 20190326 8

<h4>Objective</h4>In this article, we investigated the effects of external human-machine interfaces (eHMIs) on pedestrians' crossing intentions.<h4>Background</h4>Literature suggests that the safety (i.e., not crossing when unsafe) and efficiency (i.e., crossing when safe) of pedestrians' interactions with automated vehicles could increase if automated vehicles display their intention via an eHMI.<h4>Methods</h4>Twenty-eight participants experienced an urban road environment from a pedestrian's ...[more]

PMID: 30912985

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Project description:BackgroundMultiple distraction indicators have been applied to measure street-crossing distraction but their validities in predicting pedestrian safety are poorly understood.MethodsBased on a video-based observational study, we compared the validity of four commonly used distraction indicators (total duration of distraction while crossing a street, proportion of distracted time over total street-crossing time, duration of the longest distraction time, and total number of distractions) in predicting three pedestrian safety outcomes (near-crash incidence, frequency of looking left and right, and speed crossing the street) across three types of distraction (mobile phone use, talking to other pedestrians, eating/drinking/smoking). Change in Harrell's C statistic was calculated to assess the validity of each distraction indicator based on multivariable regression models including only covariates and including both covariates and the distraction indicator.ResultsHeterogeneous capacities in predicting the three safety outcomes across the four distraction indicators were observed: 1) duration of the longest distraction time was most predictive for the occurrence of near-crashes and looks left and right among pedestrians with all three types of distraction combined and talking with other pedestrians (Harrell's C statistic changes ranged from 0.0310 to 0.0335, P < 0.05), and for the occurrence of near-crashes for pedestrians involving mobile phone use (Harrell's C statistic change: 0.0053); 2) total duration of distraction was most predictive for speed crossing the street among pedestrians with the combination and each of the three types of distraction (Harrell's C statistic changes ranged from 0.0037 to 0.0111, P < 0.05), frequency of looking left and right among pedestrians distracted by mobile phone use (Harrell's C statistic change: 0.0115), and the occurrence of near-crash among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0119); and 3) the total number of distractions was the most predictive indicator of frequency of looking left and right among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0013). Sensitivity analyses showed the results were robust to change in grouping criteria of the four distraction indicators.ConclusionsFuture research should consider the pedestrian safety outcomes and type of distractions to select the best distraction indicator.

Dataset Information

External Human-Machine Interfaces on Automated Vehicles: Effects on Pedestrian Crossing Decisions.

Publications

External Human-Machine Interfaces on Automated Vehicles: Effects on Pedestrian Crossing Decisions.

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