Publication: Estimation of the optimum speed to minimize the driver stress based on the previous behavior
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Identifiers
ISSN: 2194-5357 (print)
ISSN: 2194-5365 (online)
ISBN: 978-3-319-40113-3 (print)
ISBN: 978-3-319-40114-0 (online)
UXXI: CC/0000026638
Publication date
2016-05-28
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Springer Berlin / Heidelberg
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Abstract
Stress is one of the most important factors in car accidents. When the driver is in this mental state, their skills and abilities are reduced. In this paper, we propose an algorithm to predict stress level on a road. Prediction model is based on deep learning. The stress level estimation considers the previous driver's driving behavior before reaching the road section, the road state (weather and traffic), and the previous driving made by the driver. We employ this algorithm to build a speed assistant. The solution provides an optimum average speed for each road stage that minimizes the stress. Validation experiment has been conducted using five different datasets with 100 samples. The proposal is able to predict the stress level given the average speed by 84.20% on average. The system reduces the heart rate (15.22%) and the aggressiveness of driving. The proposed solution is implemented on Android mobile devices and uses a heart rate chest strap.
Description
Proceedings of 7th International Symposium on Ambient Intelligence (ISAmI 2016), Seville, Spain on June 1st–3rd
Keywords
Intelligent transport system, Stress driver, Driving assistant, Deep learning, Particle Swarm Optimization, Android, Mobile computing
Bibliographic citation
Lindgren H., et al. (eds.). Ambient intelligence: software and applications: 7th International Symposium on Ambient Intelligence (ISAmI 2016). Proceedings. Berlin: Springer, 2016 (pp. 31-39). (Advances in Intelligent Systems and Computing, v. 476)