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Open AccessArticle

Efficient Paradigm to Measure Street-Crossing Onset Time of Pedestrians in Video-Based Interactions with Vehicles

1
Mercedes-Benz AG, Leibnizstraße 2, 71032 Böblingen, Germany
2
Department of Human Factors, Ulm University, Albert-Einstein-Allee 41, 89081 Ulm, Germany
3
Mercedes-Benz R&D NA, 309 N Pastoria Ave, Sunnyvale, CA 94085, USA
*
Author to whom correspondence should be addressed.
Information 2020, 11(7), 360; https://doi.org/10.3390/info11070360
Received: 28 May 2020 / Revised: 26 June 2020 / Accepted: 29 June 2020 / Published: 11 July 2020
With self-driving vehicles (SDVs), pedestrians can no longer rely on a human driver. Previous research suggests that pedestrians may benefit from an external Human–Machine Interface (eHMI) displaying information to surrounding traffic participants. This paper introduces a natural methodology to compare eHMI concepts from a pedestrian’s viewpoint. To measure eHMI effects on traffic flow, previous video-based studies instructed participants to indicate their crossing decision with interfering data collection devices, such as pressing a button or slider. We developed a quantifiable concept that allows participants to naturally step off a sidewalk to cross the street. Hidden force-sensitive resistor sensors recorded their crossing onset time (COT) in response to real-life videos of approaching vehicles in an immersive crosswalk simulation environment. We validated our method with an initial study of N = 34 pedestrians by showing (1) that it is able to detect significant eHMI effects on COT as well as subjective measures of perceived safety and user experience. The approach is further validated by (2) replicating the findings of a test track study and (3) participants’ reports that it felt natural to take a step forward to indicate their street crossing decision. We discuss the benefits and limitations of our method with regard to related approaches. View Full-Text
Keywords: pedestrians; self-driving vehicles; automated driving; external human-machine interface; test methods; evaluation; user studies pedestrians; self-driving vehicles; automated driving; external human-machine interface; test methods; evaluation; user studies
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  • Externally hosted supplementary file 1
    Doi: 10.1145/3313831.3376484
    Link: https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3313831.3376484&file=pn5519vf.mov&download=true
    Description: Video S1: status+intent eHMI. Reproduced with permission from Faas, Kao and Baumann, A longitudinal video study on communicating status and intent for self-driving vehicle–pedestrian interaction, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ‘20); published by ACM, 2020, doi: 10.1145/3313831.3376484.
MDPI and ACS Style

Faas, S.M.; Mattes, S.; Kao, A.C.; Baumann, M. Efficient Paradigm to Measure Street-Crossing Onset Time of Pedestrians in Video-Based Interactions with Vehicles. Information 2020, 11, 360. https://doi.org/10.3390/info11070360

AMA Style

Faas SM, Mattes S, Kao AC, Baumann M. Efficient Paradigm to Measure Street-Crossing Onset Time of Pedestrians in Video-Based Interactions with Vehicles. Information. 2020; 11(7):360. https://doi.org/10.3390/info11070360

Chicago/Turabian Style

Faas, Stefanie M.; Mattes, Stefan; Kao, Andrea C.; Baumann, Martin. 2020. "Efficient Paradigm to Measure Street-Crossing Onset Time of Pedestrians in Video-Based Interactions with Vehicles" Information 11, no. 7: 360. https://doi.org/10.3390/info11070360

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