250 Pedestrian-vehicle interactions: early results from the Australian naturalistic driving study (ands). (1st September 2016)

• Record Type: Journal Article
• Authors: Mattos, Garrett; Grzebieta, Raphael; Williamson, Ann; Olivier, Jake; Eusebio, Jan; Zheng, Wu Yi; Wall, John; Charlton, Jude; Lenné, Mike; Haley, Jack; Barnes, Ben; Rakotonirainy, Andry; Woolley, Jeremy; Senserrick, Teresa; Young, Kristie; Haworth, Narelle; Regan, Mike; Cockfield, Samanatha; Healy, David; Cavallo, Antonietta; Stefano, Marilyn Di; Wong, Hee Loong; Cameron, Iain; Cornish, Michael; Baird, Christine
• Is Part Of: Injury prevention. Volume 22(2016)Supplement 2
• Page Start: A91
• Page End: A92
• Publication Date: 2016-09-01
• Subjects: Pedestrian -- Crash -- Naturalistic Driving Study -- ANDS -- Typology; Children's accidents -- Prevention -- Periodicals; Accidents -- Prevention -- Periodicals; 617.1
• DOI: 10.1136/injuryprev-2016-042156.250
• Languages: English
• ISSNs: 1353-8047

Abstract

Abstract : Background: Typologies have been defined previously for pedestrian-vehicle interactions and are primarily based on retrospective analysis of crash data. The naturalistic driving study currently underway in Australia makes it possible to study pedestrian-vehicle interaction events that would not otherwise be identified in the crash data. This work evaluates the feasibility of using automated, manual, and semi-automated methods to identify pedestrian-vehicle interaction events. Methods: Sensors and cameras were installed on the vehicles of volunteers in and around two major Australian cities which recorded their natural driving behaviour for 4 months. Forward video from select vehicles was reviewed independently by two reviewers to identify potential pedestrian-vehicle interaction events from which a typology of behaviours was formulated. These events served as the gold standard against which select automated and semi-automated methods of identification were assessed. Results: A prototype typology of pedestrian-vehicle interaction events was formulated using naturalistic driving data and categorised in terms of risk of being struck. Some case scenarios will be discussed. The feasibility of using select automated, semi-automated, and manual methods to identify these events was also evaluated. Conclusions: This work provides a first look at using Australian naturalistic driving data to study the interactions between vehicles and pedestrians. These findings will assist in the development of methods that can be used to most effectively answer research questions pertaining to interactions between vehicles and pedestrians as well as other vulnerable road users in the future.