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document type:
journal article 
publication status:
accepted for publication 
authors:
Marcus Müller, Michael Botsch, Dennis Böhmländer and Wolfgang Utschick 
title:
Machine Learning Based Prediction of Crash Severity Distributions for Mitigation Strategies 
abstract:
In road traffic, critical situations pass by as quickly as they appear. Within the blink of an eye, one has to come to a decision, which can make the difference between a low severity, high severity or fatal crash. Because time is important, a machine learning driven Crash Severity Predictor (CSP) is presented which provides the estimated crash severity distribution of an imminent crash in less than 0.2ms. This is 𝟔𝟔𝟔𝟔⋅ 𝟏𝟏𝟏𝟏𝟑 times faster compared to predicting the same distribution through comp...    »
 
keywords:
crash severity, vehicle safety, reliable prediction, machine learning 
journal title:
Journal of Advances in Information Technology (JAIT) 
publisher:
International Association of Computer Science and Information Technology (IACSIT) 
E-ISSN:
1798-2340 
year:
2018 
month:
February 
volume:
Volume 9, No.1, February 2018 
issue:
Volume 9, No.1 
pages:
n/a 
language:
en 
TUM-institution:
Professur für Methoden der Signalverarbeitung 
ingested:
29.01.2018 
last change:
29.01.2018 
accepted:
07.11.2017 
format:
text