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Towards Audio-based Distraction Estimation in the Car

Authors:
Svenja Borchers
Denis Martin
Sarah Mieskes
Stefan Rieger
Cristóbal Curio
Victor Fäßler

Keywords: distraction; auditory; automotive; driver; cognitive model

Abstract:
Distraction of the driver is one of the most frequent causes for car accidents. We aim for a computational cognitive model predicting the driver’s degree of distraction during driving while performing a secondary task, such as talking with co-passengers. The secondary task might cognitively involve the driver to differing degrees depending on the topic of the conversation or the number of co-passengers. In order to detect these subtle differences in everyday driving situations, we aim to analyse in-car audio signals and combine this information with head pose and face tracking information. In the first step, we will assess driving, video and audio parameters reliably predicting cognitive distraction of the driver. These parameters will be used to train the cognitive model in estimating the degree of the driver’s distraction. In the second step, we will train and test the cognitive model during conversations of the driver with co-passengers during active driving. This paper describes the work in progress of our first experiment with preliminary results concerning driving parameters corresponding to the driver’s degree of distraction. In addition, the technical implementation of our experiment combining driving, video and audio data and first methodological results concerning the auditory analysis will be presented. The overall aim for the application of the cognitive distraction model is the development of a mobile user profile computing the individual distraction degree and being applicable also to other systems.

Pages: 187 to 190

Copyright: Copyright (c) IARIA, 2015

Publication date: March 22, 2015

Published in: conference

ISSN: 2308-4197

ISBN: 978-1-61208-390-2

Location: Nice, France

Dates: from March 22, 2015 to March 27, 2015