The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/15134037

Onkhar, Vishal; Bazilinskyy, Pavlo; Stapel, J.C.J. (Jork); Dodou, Dimitra; Gavrila, Dariu et. al. (2022): Supplementary data for the paper 'Towards the detection of driver–pedestrian eye contact'. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/15134037.v2

Dataset

x choose version: version 1 - 2021-08-12

• Cognitive Sciences
• Automotive Engineering

Driver–pedestrian interaction, Eye contact, Eye tracking, Image recognition, Wearable devices

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by Vishal Onkhar , Pavlo Bazilinskyy , J.C.J. (Jork) Stapel , Dimitra Dodou , Dariu Gavrila , Joost de Winter

Non-verbal communication, such as eye contact between drivers and pedestrians, has been regarded as one way to reduce accident risk. So far, studies have assumed rather than objectively measured the occurrence of eye contact. We address this research gap by developing an eye contact detection method and testing it in an indoor experiment with scripted driver-pedestrian interactions at a pedestrian crossing. Thirty participants acted as a pedestrian either standing on an imaginary curb or crossing an imaginary one-lane road in front of a stationary vehicle with an experimenter in the driver’s seat. In half of the trials, pedestrians were instructed to make eye contact with the driver; in the other half, they were prohibited from doing so. Both parties’ gaze was recorded using eye trackers. An in-vehicle stereo camera recorded the car’s point of view, a head-mounted camera recorded the pedestrian’s point of view, and the location of the driver’s and pedestrian’s eyes was estimated using image recognition. We demonstrate that eye contact can be detected by measuring the angles between the vector joining the estimated location of the driver’s and pedestrian’s eyes, and the pedestrian’s and driver’s instantaneous gaze directions, respectively, and identifying whether these angles fall below a threshold of 4°. We achieved 100% correct classification of the trials involving eye contact and those without eye contact, based on measured eye contact duration. The proposed eye contact detection method may be useful for future research into eye contact.

• 2021-08-12 first online

• 2022-05-03 published, posted

4TU.ResearchData

Towards the detection of driver–pedestrian eye contact

• This research is supported by grant 016.Vidi.178.047 (“How should automated vehicles communicate with other road users?”), which is financed by the Netherlands Organisation for Scientific Research (NWO).

TU Delft, Faculty of Mechanical, Maritime and Materials Engineering