Driver drowsiness detection in the freight industry

Abstract

Driver drowsiness has over the years become a key concern to everyone involved in long distance travels especially in the freight industry. Year in, year out, the number of deaths and fatalities globally as a result of driver drowsiness keep increasing significantly. Thus, ensuring the road safety of people is of uttermost importance. One of the safety measures employed against driver drowsiness is the use of a dashboard camera. Despite the widespread adoption and growing numbers of installations of dashboard cameras (Dashcams) across the globe with even evolved technology, current dashcams are still incapable of learning how to identify different postures or gestures that indicate that a driver seems to be either distracted, drowsy or asleep while driving. Even though they have the capability of recording anything happening on the road or in the vehicle in the event of an accident, they are still unable to provide real-time warnings or triggers to the drowsy driver attempting to possibly prevent an accident from happening. They also require continuous monitoring which is ineffective due to a human’s inability to maintain sufficient attention to discern significant events, a gap that the proposed enhancement aims to fill. The proposed Machine Learning Aided Drowsiness Detection System intends to cater for the fundamental flaws in today’ dashcams. Machine learning incorporates aspects of artificial intelligence that empower systems with the ability to continuously learn and improve automatically with experience without being explicitly programmed. It triggers a new way of thinking about the current dashcams. It offers new features, such as real-time conscious monitoring and gives an alert to the driver in the case of drowsiness being detected, in addition to the pre-existing systems’ features – a visual system that not only ‘sees’, but also ‘understands’ what it’s ‘seeing’. It is an undeniable fact that the use of dash cams integrated with machine learning will offer new robust capabilities. The Drowsiness Detection System comprises co-working components of a computer vision, camera, and a special type of machine learning model based on Deep Learning using Neural Networks which excels in object detection and recognition tasks via image analysis achieving an accuracy of 96.19%. The drowsiness detection system is thus highly efficient in the identification of drowsiness from different facial features such as eyes and mouth, send out a warning or alert in the event that drowsiness is detected and be continuously trained and improved with better and more datasets. The proposed system is convenient due to its improved performance and efficiency.