论文标题
道路上的先生们：了解行人如何使用机器学习来解释自动驾驶汽车的行为
Gentlemen on the Road: Understanding How Pedestrians Interpret Yielding Behavior of Autonomous Vehicles using Machine Learning
论文作者
论文摘要
自动驾驶汽车（AV）可以通过理解行人意图来防止碰撞。我们对39名参与者进行了虚拟现实实验，并进行了测量的交叉时间（秒）和头方向（偏航度）。我们操纵了AV产生行为（不收养，慢饲养和快速收益）和AV尺寸（小，中和大）。使用机器学习方法，我们按时间将行人的头部定向变化分为6个模式簇。结果表明，行人头取向的变化受AV产生行为以及AV的大小的影响。即使汽车接近近的时候，参与者大部分时间都固定在正面。当大尺寸AV未产生时，参与者最常改变头部方向（毫无收益）。在实验后的访谈中，参与者报告说，产生行为和规模影响了他们越过和感知安全性的决定。为了使自动驾驶汽车被认为更安全和值得信赖，在设计过程中应考虑诸如大小和屈服行为之类的车辆特异性因素。
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.
