Younes Ed-doughmi
ABSTRACT
Road accidents are becoming a real global scourge because of the high number of victims involved and the severe consequences that affect road users as well as their families.
Despite the awareness campaigns on the vigilance and caution that must be undertaken on the road, deaths caused in road accidents are still increasing and are now considered as a major public health problem, more specifically in Morocco where the roads are among the most deadly.
To address this issue, vehicle manufacturers have made considerable progress in improving the intelligence and capacity of vehicles to perceive and analyze road environments to prevent accidents and secure passengers. However, with all these efforts, accident statistics show that in most cases, accidents are related to the inattention of the drivers and sometimes irresponsible behavior.
Therefore, considerable amount of research has recently been focused on the analysis and study of the general behaviors of drivers on the road, especially somnolence, as it is among the highest risk factors of accidents and is the leading cause of death on roads.
In this paper, we propose a new approach to analyze driver drowsiness by applying a new recurrent neural network architecture to frame sequences of a driver. We used a public data set to train and validate our model and applied a recurrent neural network architecture called "long short-term memory" to detect driver drowsiness.
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Index Terms
- Driver Fatigue Detection using Recurrent Neural Networks
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