[论文解读] What Is the Gaze Behavior of Pedestrians in Interactions with an Automated Vehicle When They Do Not Understand Its Intentions?

Hailong Liu, Takatsugu Hirayama+2 more|arXiv (Cornell University)|Jan 1, 2020

Human-Automation Interaction and Safety参考文献 26被引用 8

一句话总结

本研究探讨了行人与无法理解的自动驾驶汽车（AV）互动时的凝视行为，提出凝视时长越长，对AV意图的理解越低。通过将机械轮椅用作AV/MV，结合眼动追踪与主观评估，发现凝视时长与对AV意图的理解之间存在显著负相关（r = -0.53），支持了长时间凝视反映不确定性的假设。研究结果建议，eHMI应主动显示意图，并在行人持续凝视时提升信息清晰度。

ABSTRACT

Interactions between pedestrians and automated vehicles (AVs) will increase significantly with the popularity of AV. However, pedestrians often have not enough trust on the AVs , particularly when they are confused about an AV's intention in a interaction. This study seeks to evaluate if pedestrians clearly understand the driving intentions of AVs in interactions and presents experimental research on the relationship between gaze behaviors of pedestrians and their understanding of the intentions of the AV. The hypothesis investigated in this study was that the less the pedestrian understands the driving intentions of the AV, the longer the duration of their gazing behavior will be. A pedestrian--vehicle interaction experiment was designed to verify the proposed hypothesis. A robotic wheelchair was used as the manual driving vehicle (MV) and AV for interacting with pedestrians while pedestrians' gaze data and their subjective evaluation of the driving intentions were recorded. The experimental results supported our hypothesis as there was a negative correlation between the pedestrians' gaze duration on the AV and their understanding of the driving intentions of the AV. Moreover, the gaze duration of most of the pedestrians on the MV was shorter than that on an AV. Therefore, we conclude with two recommendations to designers of external human-machine interfaces (eHMI): (1) when a pedestrian is engaged in an interaction with an AV, the driving intentions of the AV should be provided; (2) if the pedestrian still gazes at the AV after the AV displays its driving intentions, the AV should provide clearer information about its driving intentions.

研究动机与目标

• 识别行人与自动驾驶汽车（AV）互动时不确定性的客观行为指标。
• 检验行人对AV驾驶意图的理解程度是否与凝视AV的时间长度相关。
• 比较行人与手动驾驶车辆（MVs）及AV互动时的凝视行为差异。
• 通过识别意图提示的最佳时机与清晰度，为AV外部人机界面（eHMIs）的设计提供依据。

提出的方法

• 使用机械轮椅作为手动驾驶车辆（MV）和自动驾驶车辆（AV），开展行人-车辆互动实验。
• 通过眼动追踪技术记录行人与MV及AV互动时的凝视行为。
• 通过互动后5级量表问卷收集行人对驾驶意图理解的主观评估。
• 使用皮尔逊相关分析及事后检验（Tukey HSD）的一元方差分析，分析凝视时长与主观理解之间的相关性。
• 比较MV与AV互动中的凝视时长模式，以分离AV特有的行为反应。
• 通过统计建模验证假设：凝视时长增加反映对AV意图理解程度降低。

实验结果

研究问题

• RQ1 在自动驾驶汽车（AV）上凝视时间越长，是否与行人对AV驾驶意图理解程度越低相关？
• RQ2 当意图不明确时，行人与手动驾驶车辆（MV）和AV互动时的凝视行为有何不同？
• RQ3 行为主观评估的AV意图理解程度是否与凝视时长存在可测量的差异？
• RQ4 凝视时长能否作为AV互动中行人不确定性的客观指标？
• RQ5 基于凝视行为，eHMI设计在时机与清晰度方面可提出哪些建议？

主要发现

• 行人对AV的凝视时长与主观理解程度之间存在显著负相关（r = -0.53，p < 0.05）。
• 方差分析结果显示，不同理解水平下的凝视时长存在高度显著差异（F = 21.15，p < 0.001），仅在相邻理解水平间存在微小例外。
• 行人对AV的凝视时间显著长于对MV的凝视，表明AV引发更长时间的视觉关注，尤其在意图不明确时。
• 对MV的凝视时长与理解程度无显著相关性，表明人类驾驶员可能比AV更具直观可读性。
• 研究发现，行人认为AV意图比MV意图更难理解，凸显AV互动中的信任鸿沟。
• 结果支持假设：对AV的长时间凝视表明不确定性，使其成为评估行人理解程度的可行客观指标。