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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.