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Authors: Martin Torstensson 1 ; Boris Duran 2 and Cristofer Englund 3

Affiliations: 1 Chalmers University of Technology, Gothenburg and Sweden ; 2 RISE Viktoria, Lindholmspiren 3A, SE 417 56 Gothenburg and Sweden ; 3 RISE Viktoria, Lindholmspiren 3A, SE 417 56 Gothenburg, Sweden, Center for Applied Intelligent Systems Research (CAISR), Halmstad University, SE 301 18 Halmstad and Sweden

Keyword(s): CNN, RNN, Optical Flow.

Abstract: 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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Torstensson, M.; Duran, B. and Englund, C. (2019). Using Recurrent Neural Networks for Action and Intention Recognition of Car Drivers. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-351-3; ISSN 2184-4313, SciTePress, pages 232-242. DOI: 10.5220/0007682502320242

@conference{icpram19,
author={Martin Torstensson. and Boris Duran. and Cristofer Englund.},
title={Using Recurrent Neural Networks for Action and Intention Recognition of Car Drivers},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2019},
pages={232-242},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007682502320242},
isbn={978-989-758-351-3},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Using Recurrent Neural Networks for Action and Intention Recognition of Car Drivers
SN - 978-989-758-351-3
IS - 2184-4313
AU - Torstensson, M.
AU - Duran, B.
AU - Englund, C.
PY - 2019
SP - 232
EP - 242
DO - 10.5220/0007682502320242
PB - SciTePress

- Science and Technology Publications, Lda.
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