Transactions of Society of Automotive Engineers of Japan
Online ISSN : 1883-0811
Print ISSN : 0287-8321
ISSN-L : 0287-8321
Research Paper
A Study on Injury Prediction for ACN Vehicles Based on Speed Limit
-Development of an Injury Prediction Algorithm Using Australian Accident Data-
Kazuhiro KubotaTetsuya NishimotoGiulio Ponte
Author information
JOURNAL FREE ACCESS

2021 Volume 52 Issue 6 Pages 1219-1226

Details
Abstract
In this study, an injury prediction algorithm for vehicle occupants was developed based on the South Australian Traffic Accident Reporting System (TARS), a traffic accident database. The algorithm uses information that can be identified from the accident location or reported by bystanders (e.g., speed limit distance from the centre of Adelaide in SA, etc) as risk factors. The best combination of factors for injury prediction was selected from 15 items based on the Akaike Information Criteria (AIC). The algorithm had an under-triage rate of less than 10% for the serious injured and an overtriage rate of 45.1% for the minor injured. This algorithm can contribute to the reduction of the number of fatalities by helping Automatic Crash Notification (ACN) systems to operate as Advanced Automatic Crash Notification (AACN) systems, or to be used by emergency medical services in on-scene triage.
Content from these authors
© 2021 Society of Automotive Engineers of Japan, Inc.
Previous article Next article
feedback
Top