Natural Vision Based Method for Predicting Pedestrian Behaviour in Urban Environments

Pavan Vasishta 1, 2 Dominique Vaufreydaz 3, 4, 1, 5 Anne Spalanzani 1, 2
2 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
5 PERVASIVE INTERACTION
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : This paper proposes to model pedestrian behaviour in urban scenes by combining the principles of urban planning and the sociological concept of Natural Vision. This model assumes that the environment perceived by pedestrians is composed of multiple potential fields that influence their behaviour. These fields are derived from static scene elements like side-walks, cross-walks, buildings, shops entrances and dynamic obstacles like cars and buses for instance. Using this model, autonomous cars increase their level of situational awareness in the local urban space, with the ability to infer probable pedestrian paths in the scene to predict, for example, legal and illegal crossings.
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Submitted on : Tuesday, July 25, 2017 - 2:32:41 PM
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Pavan Vasishta, Dominique Vaufreydaz, Anne Spalanzani. Natural Vision Based Method for Predicting Pedestrian Behaviour in Urban Environments. IEEE 20th International Conference on Intelligent Transportation Systems, Oct 2017, Yokohama, Japan. ⟨10.1109/ITSC.2017.8317848⟩. ⟨hal-01561029⟩

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