Lyu Wei, Cao Yaqin, Ding Yi, Li Jingyu, Tian Kai, Zhang Hui
School of Economics and Management, Anhui Polytechnic University, Wuhu 241000, China.
Department of Nuclear Safety and Reliability Research, Chinergy Co., LTD, Beijing 100193, China.
Accid Anal Prev. 2025 Mar;211:107889. doi: 10.1016/j.aap.2024.107889. Epub 2024 Dec 9.
Future automated vehicles (AVs) are anticipated to feature innovative exteriors, such as textual identity indications, external radars, and external human-machine interfaces (eHMIs), as evidenced by current and forthcoming on-road testing prototypes. However, given the vulnerability of pedestrians in road traffic, it remains unclear how these novel AV appearances will impact pedestrians' crossing behaviour, especially in relation to their multimodal performance, including subjective perceptions, gaze patterns, and road-crossing decisions. To address this gap, this study pioneers an investigation into the influence of AVs' exterior design, in conjunction with their kinematics, on pedestrians' road-crossing perception and decision-making. A video-based eye-tracking experimental study was conducted with 61 participants who were exposed to video stimuli depicting a manipulated vehicle approaching a predefined road-crossing location on an unsignalized, two-way road. The vehicle's kinematic pattern was manipulated into yielding and non-yielding, and its external appearances were varied across five conditions: with a human driver (as a conventional vehicle), with no driver (as an AV), with text-based identity indications, with roof radar sensors, with dynamic eHMIs adjusted to vehicle kinematics. Participants' perceived clarity, crossing initiation time (CIT), crossing initiation distance (CID), and gaze behaviour during interactions were recorded and reported. The results revealed that AVs' yielding patterns play a dominant role in pedestrians' road-crossing decisions, supported by their subjective evaluations and CID. Furthermore, it was found that both textual identity indications and roof radar sensors had no significant effect on pedestrians' CIT and CID but did negatively impact their visual attention, as evidenced by heightened fixation counts and prolonged fixation durations. In contrast, the deployment of eHMIs helped mitigate the visual load and perceptual confusion associated with AV's identity features, expedite road-crossing decisions in terms of both time and space, and thus improve overall communication efficiency. The practical and safety implications of these findings for future external interaction design of AVs are discussed from the perspective of vulnerable road users.
未来的自动驾驶汽车预计将具备创新的外观，如文字标识、外部雷达和外部人机界面（eHMI），目前及即将进行的道路测试原型已证明了这一点。然而，考虑到行人在道路交通中的脆弱性，这些新颖的自动驾驶汽车外观将如何影响行人的过街行为，尤其是在其多模态表现方面，包括主观感知、注视模式和过街决策，仍不清楚。为了填补这一空白，本研究率先调查自动驾驶汽车的外观设计及其运动学对行人过街感知和决策的影响。我们进行了一项基于视频的眼动追踪实验研究，61名参与者观看了视频刺激，视频中展示了一辆在无信号灯的双向道路上驶向预定义过街位置的受控车辆。车辆的运动模式被操纵为让行和不让行，其外观在五种情况下有所不同：有人类驾驶员（作为传统车辆）、无驾驶员（作为自动驾驶汽车）、有基于文字的标识、有车顶雷达传感器、有根据车辆运动学调整的动态eHMI。记录并报告了参与者在交互过程中的感知清晰度、过街起始时间（CIT）、过街起始距离（CID）和注视行为。结果表明，自动驾驶汽车的让行模式在行人的过街决策中起主导作用，这得到了他们的主观评价和CID的支持。此外，研究发现，文字标识和车顶雷达传感器对行人的CIT和CID均无显著影响，但确实对他们的视觉注意力产生了负面影响，这表现为注视次数增加和注视持续时间延长。相比之下，eHMI的部署有助于减轻与自动驾驶汽车身份特征相关的视觉负荷和感知混乱，在时间和空间方面加快过街决策，从而提高整体通信效率。从弱势道路使用者的角度讨论了这些发现对未来自动驾驶汽车外部交互设计的实际和安全意义。
