Hochman Michal, Parmet Yisrael, Oron-Gilad Tal
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beersheba, Israel.
Front Psychol. 2020 Dec 3;11:585280. doi: 10.3389/fpsyg.2020.585280. eCollection 2020.
This study explored pedestrians' understanding of Fully Autonomous Vehicles (FAVs) intention to stop and what influences pedestrians' decision to cross the road over time, i.e., learnability. Twenty participants saw fixed simulated urban road crossing scenes with a single FAV on the road as if they were pedestrians intending to cross. Scenes differed from one another in the FAV's, distance from the crossing place, its physical size, and external Human-Machine Interfaces (e-HMI) message by background color (red/green), message type (status/advice), and presentation modality (text/symbol). Eye-tracking data and decision measurements were collected. Results revealed that pedestrians tend to look at the e-HMI before making their decision. However, they did not necessarily decide according to the e-HMIs' color or message type. Moreover, when they complied with the e-HMI proposition, they tended to hesitate before making the decision. Overall, a learning effect over time was observed in all conditions regardless of e- HMI features and crossing context. Findings suggest that pedestrians' decision making depends on a combination of the e-HMI implementation and the car distance. Moreover, since the learning curve exists in all conditions and has the same proportion, it is critical to design an interaction that would encourage higher probability of compatible decisions from the first phase. However, to extend all these findings, it is necessary to further examine dynamic situations.
本研究探讨了行人对全自动驾驶汽车（FAV）停车意图的理解，以及随着时间推移，哪些因素会影响行人过马路的决定，即学习能力。20名参与者观看了固定的模拟城市道路交叉场景，道路上有一辆FAV，就好像他们是打算过马路的行人。场景之间的差异在于FAV与交叉路口的距离、其物理尺寸以及通过背景颜色（红色/绿色）、消息类型（状态/建议）和呈现方式（文本/符号）的外部人机界面（e-HMI）消息。收集了眼动追踪数据和决策测量数据。结果显示，行人在做出决定之前倾向于查看e-HMI。然而，他们不一定根据e-HMI的颜色或消息类型来做决定。此外，当他们遵循e-HMI的提议时，往往会在做出决定前犹豫不决。总体而言，无论e-HMI特征和交叉路口情况如何，在所有条件下都观察到了随着时间推移的学习效应。研究结果表明，行人的决策取决于e-HMI的实施情况和汽车距离的综合因素。此外，由于在所有条件下都存在学习曲线且比例相同，因此设计一种能够在第一阶段鼓励更高概率做出兼容决策的交互方式至关重要。然而，为了扩展所有这些发现，有必要进一步研究动态情况。
