Driver Assistant system for sleep prevention with automatic braking mechanism

Driver drowsiness/fatigue is an important cause of combination-unit truck crashes. Drowsy driver detection methods can form the basis of a system to potentially reduce accidents related to drowsy driving.Nowadays, more and more professions require long-term concentration. Drivers must keep a close e

Project Title

Driver Assistant system for sleep prevention with automatic braking mechanism

Project Area of Specialization

Artificial Intelligence

Project Summary

Driver drowsiness/fatigue is an important cause of combination-unit truck crashes. Drowsy driver detection methods can form the basis of a system to potentially reduce accidents related to drowsy driving.Nowadays, more and more professions require long-term concentration. Drivers must keep a close eye on the road, so they can react to sudden events immediately. Driver fatigue often becomes a direct cause of many traffic accidents. Therefore, there is a need to develop the systems that will detect and notify a driver of her/him bad psychophysical condition, which could significantly reduce the number of fatigue-related car accidents. However, the development of such systems encounters many difficulties related to fast and proper recognition of a driver’s fatigue symptoms. One of the technical possibilities to implement driver drowsiness detection systems is to use the vision-based approach. This article presents the currently used driver drowsiness detection systems. The technical aspects of using the vision system to detect a driver drowsiness are also discussed.

Project Objectives

•  Driver drowsiness detection is a car safety technology which helps to save the life of the driver by preventing accidents when the driver is getting drowsy.
•  The main objective is to first design a system to detect driver’s drowsiness by continuously monitoring retina of the eye.
• The system works in spite of driver wearing spectacles and in various lighting conditions.
• To alert the driver on the detection of drowsiness by using buzzer or alarm.
• Speed of the vehicle can be reduced.
•  Traffic management can be maintained by reducing the accidents.

Project Implementation Method

We have installed a night vision camera which works in every light condition. In this project we detect the eyes of the driver for which we have used python language, in python we have used open Cv library for computer vision. To detect driver's eyes we have used haarcascade, for the facial landmarks we have used dlib (facial landmark predictor). In this system we have an alarm and breaking mechanism which continously takes reading from the distance sensor.

Benefits of the Project

• Driver's life is no more at risk.
• Almost 25% accidents take place because the driver falls asleep,
• In every accident approx $2500 are used for the repairing.

Technical Details of Final Deliverable

Our purpose in this project is to detect driver's eyes for that we have connected a night vision camera to raspberry pi, all the processing is taking place in raspberry pi. The alogorithm we have designed is in python language in which we have used extra binding like open Cv, dlib (facial landmark predictor), haarcascade. Haarcascade is used to detect driver's eyes, Dilib facial landmark monitors driver's eyes inspection ratio.

When the driver falls asleep, pi generates a signal to alarm system and activates breaking mechanism which takes data from the distance sensor and applies brake.

Final Deliverable of the Project

HW/SW integrated system

Type of Industry

Transportation

Technologies

Artificial Intelligence(AI)

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com