Space Invaders: Pedestrian Proxemic Utility Functions and Trust Zones for Autonomous Vehicle Interactions
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Space Invaders: Pedestrian Proxemic Utility Functions and Trust Zones for Autonomous Vehicle Interactions Fanta Camara1,2
· Charles Fox1,2
Accepted: 24 October 2020 © The Author(s) 2020
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 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. Keywords Proxemics · Autonomous vehicles · Trust · Pedestrians · Mathematical models of human behaviour
1 Introduction Autonomous vehicles (AVs) are claimed by many organisations to be close to commercial reality, but their lack of human behaviour understanding is raising concerns. While robotic localisation and navigation in static environments [76] and pedestrian detection [9] are well understood, AVs do not yet have the social abilities of human drivers—who can read the intentions of other road users, predict their future behaviour and then interact with them [10]. Pedestrians, unlike other road users such as cyclists, do not usually follow specific traffic rules, in particular when crossing the road at unsigned crossing points, making them especially difficult to model, predict, and interact with. Pedestrians and human drivers
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Fanta Camara [email protected] Charles Fox [email protected]
1
Institute for Transport Studies, University of Leeds, Leeds, UK
2
School of Computer Science, University of Lincoln, Lincoln, UK
communicate and interact with one another via nonverbal signals including their positions and speeds, which are used to transmit intent information as well as to make progress on the road [66]. For example, a vehicle which drives deliberately close to a pedestrian to scare them is telling them to yield, while a vehicle which maintains a larger distance from them is inviting them to cross. Recent trials of autonomous minibuses in La Rochelle (France) and Trikala (Greece) [52], highlighted the major drawback of perfectly safe self-driving c
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