Early Identify and Notify Driver’s Drowsiness by Machine Learning

Abstract

Drunkenness or exhaustion is a leading cause of car accidents, with severe implications for road safety. More fatal accidents could be avoided if fatigued drivers were warned ahead of time. Several drowsiness detection technologies to monitor for signs of inattention while driving and notifying the driver can be adopted. Sensors in self-driving cars must detect if a driver is sleepy, angry, or experiencing extreme changes in their emotions, such as anger. These sensors must constantly monitor the driver’s facial expressions and detect facial landmarks in order to extract the driver’s state of expression presentation and determine whether they are driving safely. As soon as the system detects such changes, it takes control of the vehicle, immediately slows it down, and alerts the driver by sounding an alarm to make them aware of the situation. The proposed system will be integrated with the vehicle’s electronics, tracking the vehicle’s statistics and providing more accurate results. In this paper, we have implemented real-time image segmentation and drowsiness using machine learning methodologies. In the proposed work, an emotion detection method based on Support Vector Machines (SVM) has been implemented using facial expressions. The algorithm was tested under variable luminance conditions and outperformed current research in terms of accuracy. We have achieved 83.25 % to detect the facial expression change.

Introduction

I. INTRODUCTION

This. Drowsiness is one of the primary drivers of genuine car crashes in our day-to-day lives. The National Highway Traffic Safety Administration indicated that around 150 individuals are murdered in the United States every year due to driver tiredness. 71,000 harmed and 12.5 billion in misfortunes Another report showed that the U.S.A government and organizations spend about 60.4 billion every year on mishaps identified with drowsiness. Due to drowsiness, it costs buyers about 16.4 billion in property harm, well-being cases, time, and efficiency misfortunes.

A. Machine Learning

1. Machine Learning is a domain sof Artificial Intelligence (A.I). A computer program which learns from experience is called a machine learning program or simply a learning program . Machine Learning has four types of Learning Methods, 1)Supervised Learning, 2)Un-Supervised Learning, 3) Semi-Supervised Learning, 4) Reinforcement Learning.

2. In this test the camera stays directly facing the driver while the driver rotates his head to the right, returns to the starting position and then rotates the head to the left and back to center again. We are again looking to see what is the angle of breaking for our head/eye detection algorithm and what is its consistency across the frames.The showing the tests setup and results. Difference in detection angles is much smaller than in previous test, only around 5 degrees. It seems that the algorithm is consistently and reliably detecting drivers eyes as long as the driver is facing towards the camera and his gaze does not deviate more then 35 degrees in both directions from the front facing position.

II. LITERATURE SURVEY

A. Early Identify and Notify Drowsines by Machine Learning

1. Drowsiness is one of the primary drivers of genuine car crashes in our day-by-day lives. The National Highway Traffic Safety Administration indicated that around 150 individuals are murdered in the United States every year due to driver tiredness. 71,000 harmed and $12.5 billion in misfortunes [1].
2.  Another report [2] showed that the U.S.A government and organizations spend about $60.4 billion every year on mishaps identified with drowsiness. Due to drowsiness, it costs buyers about $16.4 billion in property harm, well-being cases, time, and efficiency misfortunes. Drive. In 2010, the National Sleep Foundation (NSF) detailed that 54% of grown-up drivers felt sluggish while driving a vehicle, and 28% were, in reality, sleeping.

B. Vehicle Tracking and Reporting System using Robust Algoorith

1. The smart vehicle tracking system can be explained as a similar version GPS tracker where it functions as similar to keep. The user of the device knows the exact location of the device by using GPS that is the common method used to view locations.
2. The disadvantage of this device is that there no prevention, therefore the criminals only have one obstacle which is tracking and if the device is found, it can be easily thrown or dismantle. Hence this can cause a major issue if the vehicle or any object is stolen.

C. Advanced Driver Assistance System For The Drowsiness Detection Using Facial Landmarks

1. For the development of the solution, it is proposed to use the Scrum methodology, which has an iterative and incremental project management method [16]. The system architecture is presented in Figure 1, it begins with the image acquisition through the webcam. Image processing begins by resizing the image and changing it to grayscale.
2. Next, the evaluation of the opening of the eye is carried out, obtaining the value of the aspect ratio of the eye (EAR, for its acronym in English Eye Aspect Ratio). Finally, the parameter obtained from the EAR is evaluated with respect to the minimum threshold allowed. If the value is lower during a certain interval of time, the alarm will sound, otherwise it will continue to process the algorithm continuously.

D. Safe Driving By Detecting Lane Discipline and Driver Drowsiness

1. The proposed system, is supposed to have two webcams, one to detect the lane and the other to monitor the face of the driver. Now, whenever the car starts, the webcam will continuously shoot video, and the system will be sampling the videos into frames of pictures. Each picture will be fed to the processor, where using hough transform, the lanes as well as the eyes will be detected as shown in the figures of the results above.
2. Now, whenever the car crosses the lane marking without signalling, either a alarm signal will sound, or else, a brake will be applied to the wheels, to slow down the speed, just in order to avoid a possible accident. Similarly, the frames of eyes, will be continuously monitored for detecting open eyes. If, the system detects more than 10 continuous frames of closed eyes, as shown in Fig.19, then again the system sounds an alarm or sends a braking signal to the engine to slow down the vehicle. On the other hand, if the closed eyes are detected for less than 10 frames, then it will not be considered as drowsiness, as it maybe a blink or for some other reason.

III. SUMMARY

It has been shown in the proposed research work that real-time implementation of Drowsiness Detection Techniques is invariant to illumination and performs well under different lighting situations. In our work, we have implemented the application of support vector machine and image processing clustering methods for real-time classifications and video analysis, which takes input from corresponding hardware. The algorithm has been implemented and tested under various input parameters. It was observed that the proposed algorithm worked with better accuracy under illumination conditions with optimum distance from the camera. In contrast, accuracy decreased with lowering of illumination and increasing distance from the camera. The overall detection ratio was 100% for image segmentation. In contrast, in emotion and gesture recognition, the overall accuracy was 83.25% considering various scenarios.

IV. ACKNOWLEDGMENTS

We express our gratitude to my guide Ms. Kalyani Akhade for her competent guidance and timely inspiration. It is our good fortune to complete our Project under her able competent guidance. This valuable guidance, suggestions, helpful constructive criticism, keeps interest in the problem during the course of presenting this Identify and Notify Driver’s Drowsiness Detection by Machine Learning project successfully. We would like to thank our Project Coordinator Prof. B. M. Borhade and all the Teaching, Non-Teaching staff of our department. We are very much thankful to Prof. Mrs. Geeta S. Navale, Head, Department of Computer Engineering and also Dr. S. D. Markande, Principal, Prof. S. A. Kulkarni, Vice principal, Sinhgad Institute of Technology and Science, Narhe for their unflinching help, support and co-operation during this project work. We would also like to thank the Sinhgad Technical Educational Society for providing access to the institutional facilities for our project work

References

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Copyright

Copyright © 2023 Siddhant Gedekar, Priya Bhole, Mrunal Joshi, Rafe Shaikh, K. O. Akhade. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.