A Deep Learning Method for Automatic Visual Attention Detection in Older Drivers [r-libre/1748]

Chikhaoui, Belkacem; Ruer, Perrine et Vallières, Évelyne F. (2019). A Deep Learning Method for Automatic Visual Attention Detection in Older Drivers. Dans How AI Impacts Urban Living and Public Health. ICOST 2019 (p. 49-60). New York City, USA : Springer, coll. « Lecture Notes in Computer Science », vol. 11862. https://doi.org/10.1007/978-3-030-32785-9_5

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Catégorie de document :	Communications dans des actes de congrès/colloques
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	This paper addresses a new problem of automatic detection of visual attention in older adults based on their driving speed. All state-of-the-art methods try to understand the on-road performance of older adults by means of the Useful Field of View (UFOV) measure. Our method takes advantage of deep learning models such as Long-short Term Memory (LSTM) to automatically extract features from driving speed data for predicting drivers' visual attention. We demonstrate, through extensive experiments on real dataset, that our method is able to predict the driver's visual attention based on driving speed with high accuracy.
Adresse de la version officielle :	https://link.springer.com/chapter/10.1007/978-3-03...
Déposant:	Chikhaoui, Belkacem
Responsable :	Belkacem Chikhaoui
Dépôt :	04 sept. 2019 17:21
Dernière modification :	24 nov. 2021 19:51

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