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Using Recurrent Neural Networks for Action and Intention Recognition of Car Drivers
Chalmers University of Technology, Sweden.
RISE - Research Institutes of Sweden, ICT, Viktoria.
RISE - Research Institutes of Sweden, ICT, Viktoria. Halmstad University, Sweden.ORCID iD: 0000-0002-1043-8773
2019 (English)In: Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, 2019, p. 232-242Conference paper, Published paper (Refereed)
Abstract [en]

Traffic situations leading up to accidents have been shown to be greatly affected by human errors. To reduce

these errors, warning systems such as Driver Alert Control, Collision Warning and Lane Departure Warning

have been introduced. However, there is still room for improvement, both regarding the timing of when a

warning should be given as well as the time needed to detect a hazardous situation in advance. Two factors that

affect when a warning should be given are the environment and the actions of the driver. This study proposes

an artificial neural network-based approach consisting of a convolutional neural network and a recurrent neural

network with long short-term memory to detect and predict different actions of a driver inside a vehicle. The

network achieved an accuracy of 84% while predicting the actions of the driver in the next frame, and an

accuracy of 58% 20 frames ahead with a sampling rate of approximately 30 frames per second.

Place, publisher, year, edition, pages
2019. p. 232-242
Keywords [en]
CNN, RNN, Optical Flow
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-39703DOI: 10.5220/0007682502320242OAI: oai:DiVA.org:ri-39703DiVA, id: diva2:1341329
Conference
8th International Conference on Pattern Recognition Applications and Methods
Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2019-08-08Bibliographically approved

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Publisher's full texthttps://app.dimensions.ai/details/publication/pub.1112828047

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Englund, Cristofer

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