Key points are not available for this paper at this time.
Driving a car is a complex and potentially risky activity in people's everyday life, and it requires the full involvement of physiological and cognitive resources. Any loss of these resources can cause traffic accidents. For example, drowsy driving affects the ability to adapt, predict and react to unexpected events. A solution to this problem is the adoption of Advanced Driver Assistance Systems (ADAS), which can warn the driver if sleepiness is detected. Thus, they should include a Driver Monitoring System (DMS) to understand, measure and monitor human behaviour in different scenarios. This article is focused on detecting driver drowsiness by using non-intrusive measures such as the behavioural approach, as it is the most promising solution to use in real vehicles. The developed framework allows the extraction of drowsiness-related measures by analysing the driver's face with a standard camera. First, a face detection stage identifies the driver face in a video frame. Then, a set of facial landmarks locations are identified. These landmark points are used to estimate the head orientation and to detect when a blink occurs. By monitoring properly defined ocular variables, the degree of driver drowsiness is detected through a Fuzzy Inference System (FIS).
Salzillo et al. (Sun,) studied this question.