Car Assistance System with Drowsiness Detection, Lane Detection and Speed Monitoring

Our proposed model, entitled “Car assistance system with drowsiness detection, lane detection and speed monitoring,” not only prevents accidents but also saves lives. As the statistics demonstrates, car accidents are one of the leading causes of death worldwide. A great volume of study has been done to show how, if applied correctly, such a technology might lead to a significant decrease in such accidents and a reduction in the number of deaths caused by the same cause. This study covers everything from how this could be implemented to how it should be implemented, as well as future scope and developments that could be made, as well as potential constraints and how to overcome them. Driver drowsiness detection, lane detection, and speed monitoring system are three components of the proposed system. To create a working system for detecting driver drowsiness, we used Python with OpenCV, which was required to import all of the Haar cascade files required to detect and train face models so as to achieve the desired result, as well as Pygame, TensorFlow, and Keras, all of which will be discussed in detail in the report, explaining every step taken to achieve the desired result. To accomplish lane detection part of our proposed system, we’ll use the OpenCv package in python. It will be implemented by using the concept of frame mask, image thresholding and Hough transformation. The other functional subsystem is an IoT model which will be implemented and demonstrated through a display board and also a working code for the same using python.