文章基本信息

标题：Efficient Paradigm to Measure Street-Crossing Onset Time of Pedestrians in Video-Based Interactions with Vehicles
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作者：Stefanie M. Faas ; Stefan Mattes ; Andrea C. Kao 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2020
卷号：11
期号：7
页码：360-380
DOI：10.3390/info11070360
出版社：MDPI Publishing
摘要：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.
关键词：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