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Towards Affect-Aware Vehicles for Increasing Safety and Comfort: Recognizing Driver Emotions from Audio Recordings in a Realistic Driving Study

Requardt, Alicia and Ihme, Klas and Wilbrink, Marc and Wendemuth, Andreas (2020) Towards Affect-Aware Vehicles for Increasing Safety and Comfort: Recognizing Driver Emotions from Audio Recordings in a Realistic Driving Study. IET Intelligent Transport Systems, 14 (10), pp. 1265-1277. Institution of Engineering and Technology (IET). doi: 10.1049/iet-its.2019.0732. ISSN 1751-956X.

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Official URL: https://digital-library.theiet.org/content/journals/10.1049/iet-its.2019.0732

Abstract

For vehicle safety, the in-time monitoring of the driver and assessing his/her state is a demanding issue. Frustration can lead to aggressive driving behaviours, which play a decisive role in up to one-third of fatal road accidents. Consequently, the authors present the automatic analysis of the emotional driver states of frustration, anxiety, positive and neutral. Based on experiments with normal drivers within cars in real-world (low expressivity) situations, they use speech data, as speech can be recorded with zero invasiveness and comes naturally in driving situations. A careful selection of speech features, subject data identification, hyper-parameter optimisation, and machine learning algorithms was applied for this difficult 4-emotion-class detection problem, where the literature hardly reports results above chance level. In-car assistance demands real-time computing. A very detailed analysis yields best results with relatively small random forests, and with an optimal feature set containing only 65 features (6.51% of the standard emobase feature set) which outperformed all other feature sets, producing 35.38% unweighted average recall (53.26% precision) with low computational effort, and also reducing the inevitably high confusion of ‘neutral’ with low-expressed emotions. This result is comparable to and even outperforming other reported studies of emotion recognition in the wild. Their work, therefore, triggers adaptive automotive safety applications.

Item URL in elib:https://elib.dlr.de/128661/
Document Type:Article
Title:Towards Affect-Aware Vehicles for Increasing Safety and Comfort: Recognizing Driver Emotions from Audio Recordings in a Realistic Driving Study
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Requardt, Aliciaalicia.requardt (at) ovgu.deUNSPECIFIED
Ihme, KlasKlas.Ihme (at) dlr.dehttps://orcid.org/0000-0002-7911-3512
Wilbrink, Marcmarc.wilbrink (at) dlr.dehttps://orcid.org/0000-0002-7550-8613
Wendemuth, AndreasOtto-von-Guericke-Universität MagdeburgUNSPECIFIED
Date:October 2020
Journal or Publication Title:IET Intelligent Transport Systems
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:14
DOI :10.1049/iet-its.2019.0732
Page Range:pp. 1265-1277
Publisher:Institution of Engineering and Technology (IET)
ISSN:1751-956X
Status:Published
Keywords:Empathic Vehicles; Affect-Aware Systems; User-Focused Automation; Frustration; Audio Processing; Machine Learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - NGC KoFiF
Location: Braunschweig
Institutes and Institutions:Institute of Transportation Systems > Human Factors
Institute of Transportation Systems > Development of vehicle functions
Deposited By: Ihme, Klas
Deposited On:28 Sep 2020 14:10
Last Modified:28 Sep 2020 14:10

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