Abstract
Today, human drivers remain the main cause of traffic accidents. While not always being the main accident reasons, drowsiness and fatigue are often indirect reasons for accidents. As a result, it is still actual to classify the driver’s state (drowsy or awake) while driving. The most popular approaches to driver’s state classification today are the application of special medical equipment (which gives the highest results) or machine vision techniques. However, the former are nearly impossible for mass implementation due to the complexity and high costs, and the latter suffer from continuously varying lighting, which is the case for moving vehicles especially in dark conditions when the identification of the driver’s state is even more important. Previous research has shown the fundamental possibility of identification of the driver’s state based on an analysis of vehicle speed. However, its results were not very high. In this work, we are searching for a way to increase the quality of the classification by taking into account the driving context. The results show that (a) the classification of the vehicle’s driving context can be performed based on the analysis of its speed with sufficiently high accuracy, and (b) that the preliminary classification of the vehicle’s driving context can significantly increase the accuracy of the classification of the driver’s state based on the analysis of the vehicle speed.
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The research is due to State Research, project number 0073-2019-0005.
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Shilov, N. (2021). Driver’s State Identification Based on the Vehicle Speed Analysis Taking into Account the Driving Context. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Intelligent Systems. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-030-90321-3_75
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DOI: https://doi.org/10.1007/978-3-030-90321-3_75
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