Abstract

This work presents the development of an ADAS (advanced driving assistance system) focused on driver drowsiness detection, whose objective is to alert drivers of their drowsy state to avoid road traffic accidents. In a driving environment, it is necessary that fatigue detection is performed in a non-intrusive way, and that the driver is not bothered with alarms when he or she is not drowsy. Our approach to this open problem uses sequences of images that are 60 s long and are recorded in such a way that the subject’s face is visible. To detect whether the driver shows symptoms of drowsiness or not, two alternative solutions are developed, focusing on the minimization of false positives. The first alternative uses a recurrent and convolutional neural network, while the second one uses deep learning techniques to extract numeric features from images, which are introduced into a fuzzy logic-based system afterwards. The accuracy obtained by both systems is similar: around 65% accuracy over training data, and 60% accuracy on test data. However, the fuzzy logic-based system stands out because it avoids raising false alarms and reaches a specificity (proportion of videos in which the driver is not drowsy that are correctly classified) of 93%. Although the obtained results do not achieve very satisfactory rates, the proposals presented in this work are promising and can be considered a solid baseline for future works.

Highlights

  • Drowsiness, defined as the state of sleepiness when one needs to rest, can cause symptoms that have great impact over the performance of tasks: slowed response time, intermittent lack of awareness, or microsleeps, to name a few examples [1]

  • Our premise is the following: a camera mounted on a vehicle will record frontal images of the driver, which will be analyzed by using artificial intelligence (AI)

  • Even if the diminishing performance over skill-based tasks by the driver can be a consequence of drowsiness, it appears at a later stage and it cannot be used to detect the early symptoms of fatigue [15]

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Summary

Introduction

Drowsiness, defined as the state of sleepiness when one needs to rest, can cause symptoms that have great impact over the performance of tasks: slowed response time, intermittent lack of awareness, or microsleeps (blinks with a duration of over 500 ms), to name a few examples [1]. Continuous fatigue can cause levels of performance impairment similar to those caused by alcohol [2,3] While driving, these symptoms are extremely dangerous since they significantly increase the probabilities of drivers missing road signs or exits, drifting into other lanes or even crashing their vehicle, causing an accident [4]. One of the most reliable ways of estimating fatigue is by using electroencephalograms (EEG) in combination with electrooculograms (EOG) [16], but in real driving environments, these kinds of systems are usually rejected by drivers Their main drawback is that they require that the driver has attached electrodes around the eyes and over the head, which makes them intrusive systems that produce discomfort and rejection by drivers. We will focus on the detection of the early symptoms of drowsiness by using the driver’s state

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