Major
project(18ECP109L)
Portfolio
Driver Monitoring System
Guide:
Dr. Lakshmi Prabha P
Asst. Professor Dept. of Biomedical Engineering
​
Done By:
Saminathan M
RA1911034010008
Lalith Kumar S J
RA1911034010019
Hariharan R
RA1911034010031
Nitish Kumar M
RA1911034010038
Dept. Of Biomedical Engineering
Abstract
Every day, trafic accidents occur all over the world, and they frequently result in serious injuries and fatalities. According to statistics, drunk or reckless driving is responsible for 20-40% of these accidents. This project aimed to develop a product that could monitor the driver's health and drowsiness and ensure the safety of the vehicle. To achieve this aim to set out the following objectives to develop a prototype that can collect sensor data from the driver, monitor the driver's drowsiness using computer vision techniques, and incorporate facial recognition techniques to ensure the safety of the vehicle. The project was developed using the wifi module , which was connected to various sensors. These sensors collect data on the driver's pulse, body temperature, and breath alcohol level and also detect the crash of a vehicle, respectively. The wifi module is connected to the internet, allowing it to transmit the collected data to the Firebase database, from which it can be accessed via an app on a mobile device. In addition to the sensor data, used a web camera for computer vision techniques to monitor the driver's eye movements and detect signs of drowsiness. This was also incorporated with facial recognition to ensure an authorized driver could operate the vehicle. The project was tested on a vehicle and demonstrated successful monitoring of the driver's vital signs as well as accurate detection of drowsiness, facial recognition, and vehicle crashes. The mobile application displays data from the various sensors installed in the vehicle, including the driver's vitals, alcohol consumption, face recognition and vehicle crash report.Overall, this project has shown great potential for enhancing the safety of vehicles by monitoring the driver's health and alertness, detecting unauthorized drivers, and being notified in case of a crash.
Introduction
Traffic accidents occur frequently worldwide and are a major cause of severe injuries and fatalities. Drunk or reckless driving is responsible for 20-40% of these accidents, and the risk of accidents increases if the driver is ill or fatigued. Our project aims to use sensors and computer vision techniques to monitor the driver's health and alertness, as well as to prevent unauthorized individuals from operating the vehicle. Additionally, sensors to detect crashes, which is crucial for improving vehicle safety. The project utilizes temperature, pulse, and blood oxygen level sensors to monitor the driver's health. Computer vision techniques are used to detect signs of drowsiness, and facial recognition is employed to ensure only authorized individuals can operate the vehicle. The ADXL345 sensor is used to detect crashes, allowing the system to alert emergency services and potentially save lives. This system is connected to the internet, enabling real-time data processing and transmission to a database accessible on any device. The use of sensors and computer vision techniques not only enhances safety but also provides useful information to analyze driving patterns and identify areas for enhancement. 8 Furthermore, the project could be modified for use in commercial fleets where driver safety is essential, such as transportation companies or ride-sharing services. By combining sensor data and computer vision techniques, a more comprehensive approach to driver safety, addressing both physical and psychological factors that can contribute to crashes, is made possible. Ultimately, this project has the potential to significantly reduce the risk of accidents and improve vehicle safety. it is essential to continue exploring new ways to improve vehicle safety and reduce the number of traffic accidents on our roads.