Driver drowsiness can be a safety hazard, especially on a long trip. People often do not realize they are too tired to operate a motor vehicle until it is too late. It may become evident when your eyelids start drooping, and you have trouble keeping your head up. You may even realize that driving is so effortful that you cannot hold the steering wheel steady. Driving while drowsy or fatigued is associated with an increased crash risk and adverse effects on driving performance.

The machine learning approach is used for drowsiness detection of drivers to reduce the number of road accidents per year. Integration of machine learning algorithms into computer vision can help to detect whether drivers are feeling drowsy through video streams and facial recognition. IIT Ropar has built an algorithm that can extract facial features of drowsiness like eyes and mouths to effectively detect the real-time feeling of a driver. This is expected to reduce road accidents in a country by alerting the drivers on time. There are three techniques that the team of IIT Ropar developed-- driver's operational behavior can be tracked with the understanding of the steering wheel, accelerator or brake patterns and speed; physiological features of a driver like heart rate, head posture or pulse rate and computer vision system to recognize facial expressions.