Abstract

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 stud-
ies 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.