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Volume: 33 | Article ID: art00006
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Quantitative study of vehicle-pedestrian interactions: Towards pedestrian-adapted lighting communication functions for autonomous vehicles
  DOI :  10.2352/ISSN.2470-1173.2021.17.AVM-172  Published OnlineJanuary 2021
Abstract

This paper reports the main conclusions of a fielding observation of vehicle-pedestrian interactions at urban crosswalks, by describing the types, sequences, spatial distributions and probabilities of occurrence of the vehicle and pedestrian behaviors. This study was motivated by the fact that in a near future, with the introduction of autonomous vehicles (AVs), human drivers will become mere passengers, no longer being able to participate into the traffic interactions. With the purpose of recreating the necessary interactions, there is a strong need of new communication abilities for AVs to express their status and intentions, especially to pedestrians who constitute the most vulnerable road users. As pedestrians highly rely on the actual behavioral mechanism to interact with vehicles, it looks preferable to take into account this mechanism in the design of new communication functions. In this study, through more than one hundred of video-recorded vehicle-pedestrian interaction scenes at urban crosswalks, eight scenarios were classified with respect to the different behavioral sequences. Based on the measured position of pedestrians relative to the vehicle at the time of the significant behaviors, quantitative analysis shows that distinct patterns exist for the pedestrian gaze behavior and the vehicle slowing down behavior as a function of Vehicle-to-Pedestrian (V2P) distance and angle.

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Guoqin Zang, Shéhérazade Azouigui, Sébastien Saudrais, Olivier Peyricot, Mathieu Hebert, "Quantitative study of vehicle-pedestrian interactions: Towards pedestrian-adapted lighting communication functions for autonomous vehiclesin Proc. IS&T Int’l. Symp. on Electronic Imaging: Autonomous Vehicles and Machines,  2021,  pp 172-1 - 172-8,  https://doi.org/10.2352/ISSN.2470-1173.2021.17.AVM-172

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