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Gentlemen on the Road: Understanding How Pedestrians Interpret Yielding Behavior of Autonomous Vehicles using Machine Learning
arXiv - CS - Robotics Pub Date : 2020-05-16 , DOI: arxiv-2005.07872
Yoon Kyung Lee, Yong-Eun Rhee, Jeh-Kwang Ryu, Sowon Hahn

Autonomous vehicles (AVs) can prevent collisions by understanding pedestrian intention. We conducted a virtual reality experiment with 39 participants and measured crossing times (seconds) and head orientation (yaw degrees). We manipulated AV yielding behavior (no-yield, slow-yield, and fast-yield) and the AV size (small, medium, and large). Using machine learning approach, we classified head orientation change of pedestrians by time into 6 clusters of patterns. Results indicate that pedestrian head orientation change was influenced by AV yielding behavior as well as the size of the AV. Participants fixated on the front most of the time even when the car approached near. Participants changed head orientation most frequently when a large size AV did not yield (no-yield). In post-experiment interviews, participants reported that yielding behavior and size affected their decision to cross and perceived safety. For autonomous vehicles to be perceived more safe and trustful, vehicle-specific factors such as size and yielding behavior should be considered in the designing process.

中文翻译:

路上的绅士:了解行人如何使用机器学习解释自动驾驶汽车的让行行为

自动驾驶汽车 (AV) 可以通过了解行人的意图来防止碰撞。我们对 39 名参与者进行了虚拟现实实验,并测量了交叉时间(秒)和头部方向(偏航度)。我们操纵了 AV 产量行为(无产量、慢产量和快产量)和 AV 尺寸(小、中和大)。使用机器学习方法,我们将行人头部方向随时间的变化分为 6 组模式。结果表明,行人头部方向变化受 AV 屈服行为以及 AV 大小的影响。参与者大部分时间都盯着前方,即使汽车靠近时也是如此。当大尺寸 AV 没有屈服(无屈服)时,参与者最频繁地改变头部方向。在实验后的采访中,参与者报告说,屈服行为和大小影响了他们穿越的决定和安全感。为了让自动驾驶汽车更加安全和可信,在设计过程中应考虑车辆特定的因素,如尺寸和屈服行为。
更新日期:2020-08-18
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