Understanding pedestrian proxemic utility and trust will help autonomous vehicles to plan and control interactions with pedestrians more safely and efficiently. When pedestrians cross the road in front of human-driven vehicles, the two agents use knowledge of each other’s preferences to negotiate and to determine who will yield to the other. Autonomous vehicles will require similar understandings, but previous work has shown a need for them to be provided in the form of continuous proxemic utility functions, which are not available from previous proxemics studies based on Hall’s discrete zones. To fill this gap, a new Bayesian method to infer continuous pedestrian proxemic utility functions is proposed, and related to a new definition of ‘physical trust requirement’ (PTR) for road-crossing scenarios. The method is validated on simulation data then its parameters are inferred empirically from two public datasets. Results show that pedestrian proxemic utility is best described by a hyperbolic function, and that trust by the pedestrian is required in a discrete ‘trust zone’ which emerges naturally from simple physics. The PTR concept is then shown to be capable of generating and explaining the empirically observed zone sizes of Hall’s discrete theory of proxemics.

了解行人的近距离效用和信任将有助于自动驾驶汽车更安全，更有效地计划和控制与行人的互动。当行人在人为驾驶的车辆前横过马路时，这两个特工利用彼此的偏好知识进行协商并确定谁会屈服于对方。自动驾驶汽车也需要类似的理解，但是先前的工作表明需要以连续的近距离效用函数的形式提供它们，而先前基于霍尔离散理论的近距离研究并未提供这些功能。区域。为了填补这一空白，提出了一种新的贝叶斯方法来推断连续的行人近距离效用函数，并且该方法与针对交叉口场景的“物理信任要求”（PTR）的新定义有关。该方法在仿真数据上得到验证，然后从两个公共数据集中凭经验推断其参数。结果表明，行人近距离效用最好用双曲函数来描述，并且行人的信任在离散的“信任区域”中是必需的，而该“信任区域”是从简单的物理学自然而然地产生的。然后显示PTR概念能够产生和解释霍尔离散理论的经验观察到的区域大小。