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Authors: Marta Kasprzak, Anna Stróż, Anna Olejniczak-Serowiec. Background image source: .
Distraction and drowsiness are among main causes of road accidents, as they can severely impair driving abilities. Researchers and engineers all around the world are on their way to better understand these phenomena, as well as to develop legal or industrial solutions in order to minimize their impact on road safety.
As three of us work together in the field of driver-related safety measures, we would like to offer you a short series describing drowsiness and distraction measurements, which are most popular in research and DMS applications. Tentatively, we can split them into behavioral, physiological, and performance measures. Today we will dive into visual-based behavioral metrics, which have been thoroughly investigated – even for decades – and finally some of them may reach the market.
Let’s start from the beginning – how drowsiness and distraction can be understood in driving context? Is there any way to parameterize such concepts?
Distraction occurs when driver’s attention is directed towards non-driving activities. This may include interactions with infotainment systems, using mobile phone, talking to passengers, and many more . With the growing number of elements in the environment, both inside and outside of the car, distraction is one of the main factors in road safety. If you are an active driver, you probably have encountered such issues, for example when your mobile phone started ringing or a huge billboard caught your attention for a while.
Drowsiness can be described as a state in which the driver is sleepy or fatigued, however these aren’t exact synonyms. In such state driver’s abilities, like reaction time and decision making, are impaired . Even if the driver isn’t extremally sleepy, there is a risk of microsleeps’ occurrence. Microsleeps are a very short sleep episodes, dangerous especially because the driver might not be aware of experiencing them . Sometimes people claim to be aware of being sleepy, but it isn’t always so, and this is the place where DMS come in handy.
Distraction measurements based on visual methods rely mostly on eyes or face behaviour observations. During driving we look not only straight ahead on the road, but also at different elements connected to the driving task, like mirrors or instrument cluster; the main concern however is to capture signs of visual attention being targeted to non-driving activities. This can be done by tracking the driver’s gaze, but there are also more complex measures. Nevertheless, detecting real distraction can be a challenge.
Although these measurements can give us a lot of information about the driver’s attention, a more comprehensive approach, based on general patterns of the driver’s eye behaviour, including on-road glances, is a good solution. Its advantage lies in wider understanding of the road situation’s context, as not all off-road glances are associated with distraction .
Drowsiness measurements also use face and head observations but basically depend on features like blinking behaviours instead of gazes. These are one of the most legible indicators of sleepiness and fatigue, therefore are widely used in research and application.
All the above measures are complex issues and can be influenced by many factors. The overview explains them only briefly, as every one of them can be a subject of an article itself. Of course, these are the most popular ones, yet there are several other measures, sometimes being variants of the mentioned ones. As the interest in driving monitoring systems development is growing steadily year by year, such measures – when properly validated – are finally reaching the market, being the next huge step toward improvement in road safety. However, a robust DMS usually draws from numerous metrics tracked simultaneously and integrated.
We hope we gave you an insight on ways of measuring distraction and drowsiness. In the next part of our blog post we will provide you with a summary of yet another approach, based on physiological signals recording, such as electroencephalography (EEG) or galvanic-skin response (GSR).
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