Abstract
In this paper we present a new approach to discern and handle driver’s drowsiness. This task is usually based only on its detection, without providing any intelligent feedback appropriated to the situation of the driver, and focusing only on the eyes. The response is usually a simple beep alarm which is not enough to wake up or keep the driver awake all along the road. The innovation in our method resides first in the use of a combination of Haar cascades and deep convolutional neural networks for fast detection of the state of the driver and second, the use of an intelligent assistant agent who will follow up the driver by the front camera of his phone, and tries to take care of his security, and the security of the others.
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This work was done as part of the Erasmus Plus program, from the USTO University of Sciences and Technology of Oran to the University of Granada Spain (December 2017/May 2018 in Granada).
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This research was partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO) Project TIN2015-71873-R together with the European Fund for Regional Development (FEDER)
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The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
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Ghizlene, B., Zoulikha, M., Pomares, H. (2019). An Efficient Framework to Detect and Avoid Driver Sleepiness Based on YOLO with Haar Cascades and an Intelligent Agent. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_58
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DOI: https://doi.org/10.1007/978-3-030-20518-8_58
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