International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
ROAD SAFETY BY DETECTING DROWSINESS AND ACCIDENT USING MACHINE LEARNING 1Kashyap K. Punyawan, 2Krushnakumar H. Patle, 3Shivam R. Kale, 4Vikas K. Nagpure, 5Supriya
Sawwashere J D College of Engineering and Management, Nagpur Dr. Babasaheb Ambedkar Technological University, Lonere, India ------------------------------------------------------------------------***-----------------------------------------------------------------------should take a rest. At the same time, the road will be Abstract:
monitored by the system until it sees an accident. If an accident occurs system reports it to the emergency services, and we may act quickly to preserve the injured person’s life. By incorporating this system into a car, we can save a priceless life of a person. This effort assists individuals in remaining safe and reaching their destination. This project has the potential to save the lives of thousands of people.
The rate of road accidents is rising continuously, the majority of an accident are caused due to people's negligence and ignorance. There is a lot of work going on continuously to reduce these numbers. Many solutions are based on IoT-based applications for identifying traffic accidents, but these systems have their drawbacks. Therefore, we aim to develop a system with the objective of helping the existing system to increase accuracy. we are currently focusing on highways to reduce accidents and want to utilize this system in cars in the initial phase. We can gain accuracy by using dash cams of the cars to monitor the road to detect accident by third-party vehicles. We have also added a feature where we will continuously monitor the driver's face and detect the negligent behavior of the driver e.g sleepy and not focused. Considering that the majority of modern automobiles have a camera system will be costefficient. We are utilizing machine learning algorithms to make the system more efficient and accurate. If an accident occurs, the nearby automobile will detect and reports it to the emergency services, and we may then act quickly to preserve the life of the person who is injured.
2. Literature Survey: Vivek Upadhyay et. al. focuses to developing a system that can detect and report an accident. Their system provides methods to prevent an accident because of a speed breaker, blind turns, pits, stop signs, etc. Their Integrated Accident Prevention Detection and Response System (IAPDRS) prototype includes a GPS module to locate the accident sites and report the accident to nearby emergency services. In this proposed model they have used micro-controllers to report a message to the Emergency services like relatives, police, fire brigade, etc., whether an accident happens or not (Ex. If an accident happens then alerting message “ACCIDENT OCCURREDNEED AMBULANCE” have been sent to the ambulance controller. In the research paper[2] provides an overview of automated traffic-detecting methods for accidents. They combine various deep learning algorithms with smartphone technology, GSM and GPS, vehicle ad-hoc networking, and mobile applications for use while traveling. The techniques promptly let emergency services know about the accident region, working to save lives and lower the number of fatalities related to accidents. These techniques have some drawbacks, including limited accuracy, low reliability, and hardware problems. Therefore, there is a chance to develop effective accident detection techniques. We also investigated how the problem of drowsiness is solved so far [3] they made a system that is completely based on Micro-controller, Sensors, GSM module, GPS module, and Power Source Accident Detection and Response System (ADRs) is an auto-detection system inside a vehicle based on a microcontroller platform that detects the type of accident, performs error checking, and notifies a central control system based on Matlab, informing them via text message of the location of the nearest medical personnel,
Keywords: Accident detection, Accident of highways, CNN, Drowsiness alertness System, Machine learning,.
1. Introduction: According to different reports, traffic accidents claim the lives of around 1.3 million people each year. According to The Times Of India, the National Highways had the largest number of fatalities in road accidents in India, accounting for 34.5% (53,615 out of 1,55,622), followed by State Highways (25.1%). (39,040 deaths). In 2021, 62,967 (40.5%) people died in car accidents on other roads. According to a Times of India article, truck drivers get sleepy by driving continually to finish work on time, which is a significant cause of road accidents. Most of the time, when an accident occurs, the wounded individual does not receive sufficient emergency care; this is one of the consequences of rising deaths in road accidents. After considering all of these scenarios and causes we are developing this system which can help to reduce accidents, especially on highways. If the driver appears to be drowsy, the system sounds an alert, and we can propose that he
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