Towards the detection of driver–pedestrian eye contact

V. Onkhar, P. Bazilinskyy, J. C.J. Stapel, D. Dodou, D. Gavrila, J. C.F. de Winter*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
87 Downloads (Pure)


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.

Original languageEnglish
Article number101455
Number of pages14
JournalPervasive and Mobile Computing
Publication statusPublished - 2021


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


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