Driver Safety and Assistance System using Machine Learning & IoT

In recent years, the growth in the number of road vehicles and transportation has been exponential. This has made a major effect on our lives just like the advancement of the country. However, Road accidents are one of the serious causes of mortality. Every year around 1.3 million people lose their...

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Bibliographic Details
Main Authors: Chaturvedi, Yash, Tiwari, Naveen Kumar, Jain, Ronak, Sharma, Himanshu
Format: Conference Proceeding
Language:English
Subjects:
Convolutional Neural Networks
Drowsiness Detection
Eye Aspect Ratio
face detection
Fatigue
Human factors
Internet of Things (IoT)
Machine learning
Machine learning algorithms
OpenCV
Python
Raspberry Pi
Road accidents
Roads
Target recognition
Traffic Sign Detection
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Summary:In recent years, the growth in the number of road vehicles and transportation has been exponential. This has made a major effect on our lives just like the advancement of the country. However, Road accidents are one of the serious causes of mortality. Every year around 1.3 million people lose their lives as a result of road accidents and driver drowsiness is to be blamed in major cases. The delayed notice of emergency services and systems often makes it more difficult to protect lives. This paper aims to design a Machine Learning & Internet of Things (IoT) based robust alert system that could detect the possible signs of a road accident during drowsiness as well as daydreaming and alert the driver and emergency services to avoid the cause of happening by using different methods of machine learning algorithms. Two distinct methods for drowsiness detection and Traffic sign detection using convolutional neural networks have been discussed.
ISSN:2768-5330
DOI:10.1109/ICICCS53718.2022.9788289