Queensland University of Technology, Centre for Accident Research and Road Safety - Queensland (CARRS-Q), 130 Victoria Park Road, Kelvin Grove 4059, Australia; Université Paris Cité, Univ Gustave Eiffel, LaPEA, Boulogne-Billancourt F-92100, France; Univ Gustave Eiffel, Université Paris Cité, LaPEA, Versailles F-78000, France.
Queensland University of Technology, Centre for Accident Research and Road Safety - Queensland (CARRS-Q), 130 Victoria Park Road, Kelvin Grove 4059, Australia.
Accid Anal Prev. 2023 Nov;192:107246. doi: 10.1016/j.aap.2023.107246. Epub 2023 Aug 17.
In road traffic, mental overload often leads to a failure to notice new and distinctive stimuli. Such phenomenon is known as 'inattentional blindness'. Safe and efficient interaction between automated vehicles (AVs) and pedestrians is expected to rely heavily on external human-machine interfaces (eHMIs), a tool AVs are equipped with to communicate their intentions to pedestrians. This study seeks to explore the phenomenon of 'inattentional blindness' in the context of pedestrian-AV interactions. Specifically, the aim is to understand the effects of a warning eHMI on pedestrians' crossing decisions when they are engaged in a secondary task. In an experiment study with videos of pedestrian crossing scenarios filmed from the perspective of the crossing pedestrian, participants had to decide the latest point at which they would be willing to cross the road in front of an AV with an eHMI vs. an AV without an eHMI. Participants were also asked to predict the future behavior of the AV. 125 female and 9 male participants aged between 18 and 25 completed the experiment and a follow-up questionnaire. It was found that the presence of a warning eHMI on AVs contributes to a clearer understanding of pedestrians' inferences about the intention of AVs and helps deter late and dangerous crossing decisions made by pedestrians. However, the eHMI fail to help pedestrians avoid such decisions when they face a high mental workload induced by secondary task engagement.
在道路交通中，精神负荷过重往往会导致人们无法注意到新的和独特的刺激。这种现象被称为“疏忽性盲视”。安全高效的自动驾驶汽车（AV）与行人交互，预计将高度依赖于外部人机界面（eHMI），这是 AV 配备的一种工具，用于向行人传达其意图。本研究旨在探索行人和 AV 交互中的“疏忽性盲视”现象。具体来说，目的是了解当行人在执行次要任务时，警告 eHMI 对其穿越决策的影响。在一项基于行人穿越场景视频的实验研究中，参与者必须决定在有和没有 eHMI 的 AV 前，他们最晚愿意在何处过马路。参与者还被要求预测 AV 的未来行为。125 名女性和 9 名年龄在 18 至 25 岁之间的参与者完成了实验和后续问卷。研究结果表明，AV 上的警告 eHMI 有助于更清楚地了解行人对 AV 意图的推断，并有助于阻止行人做出危险的迟到穿越决策。然而，当行人面临由次要任务引起的高精神负荷时，eHMI 无法帮助他们避免此类决策。
