250 Pedestrian-vehicle interactions: early results from the Australian naturalistic driving study (ands)
| Autor: | Kristie Lee Young, Iain Cameron, Michael G. Lenné, Narelle Haworth, Ann Williamson, Michael Cornish, Garrett Mattos, Raphael H Grzebieta, J L Charlton, Andry Rakotonirainy, Samanatha Cockfield, Jeremy Woolley, Jan Eusebio, Michael Arthur Regan, Jake Olivier, Ben Barnes, Antonietta Cavallo, David Healy, Wu Yi Zheng, Jack Haley, John Wall, Hee Loong Wong, Teresa Senserrick, Christine Baird, Marilyn Di Stefano |
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| Rok vydání: | 2016 |
| Předmět: |
Typology
Engineering business.industry Public Health Environmental and Occupational Health 030229 sport sciences Pedestrian Data science 03 medical and health sciences Identification (information) 0302 clinical medicine Early results Research questions 030212 general & internal medicine Crash data Naturalistic driving business Simulation Road user |
| Zdroj: | Injury Prevention. 22:A91.2-A92 |
| ISSN: | 1475-5785 1353-8047 |
| DOI: | 10.1136/injuryprev-2016-042156.250 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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