Development of a Pedestrian Injury Prediction Model for Potential Use in an Advanced Automated Crash Notification System
Advanced Automated Crash Notification (AACN) systems can inform emergency services of a serious road crash with minimal delay, giving the precise location of the crash, and transmitting key information from the vehicle’s event data recorder, including: the crashed vehicle’s delta-V, occupant seatbelt use, airbag deployment and travelling speed. This information can be used to determine the likelihood of serious injury within the crashed vehicle using a suitable injury prediction algorithm. The focus of this paper is to present a proof of concept AACN pedestrian injury prediction model using South Australia crash data.
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Supplemental Notes:
- Extended abstract only
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Authors:
- Ponte, G
- Nishimoto, T
- Conference:
- Publication Date: 2017-10
Media Info
- Pagination: 3p
- Monograph Title: Proceedings of the 2017 Australasian Road Safety Conference, 10-12 October, Perth, Australia
Subject/Index Terms
- TRT Terms: Air ambulance services; Air ambulances; Ambulances; Crash records; Emergency vehicles; Fire vehicles; Injuries; Injury severity; Pedestrians; Prevention; Vehicle occupants; Vehicle to infrastructure communications
- Uncontrolled Terms: Safe systems (road users)
- Geographic Terms: Australia; South Australia
- ATRI Terms: Crash record; Emergency services; Injury severity; Pedestrian; Vehicle occupant; Vehicle to roadside communications
- ITRD Terms: 8735: Intelligent transport system; 1623: Severity (accid, injury)
- Subject Areas: Passenger Transportation; Pedestrians and Bicyclists; Safety and Human Factors; I83: Accidents and the Human Factor; I92: Vehicle Comfort;
Filing Info
- Accession Number: 01661762
- Record Type: Publication
- Source Agency: ARRB
- Files: ITRD, ATRI
- Created Date: Mar 1 2018 10:02AM