Driver Drowsiness Detection System for Accident Prevention

In recent years, the casualties of traffic accidents caused by driving cars have been gradually increasing. In particular, there are more serious injuries and deaths than minor injuries, and the damage due to major accidents is increasing. In particular, heavy cargo trucks and high-speed bus accidents that occur during driving in the middle of the night have emerged as serious social problems. Therefore, in this study, a drowsiness prevention system was developed to prevent large-scale disasters caused by traffic accidents. In this study, machine learning was applied to predict drowsiness and improve drowsiness prediction using facial recognition technology and eye-blink recognition technology. Additionally, a CO2 sensor chip was used to detect additional drowsiness. Speech recognition technology can also be used to apply Speech to Text (STT), allowing a driver to request their desired music or make a call to avoid drowsiness while driving

This project is about making cars more intelligent and interactive which may notify or resist user under unacceptable conditions, they may provide critical information of real time situations to rescue or police or owner himself. Driver fatigue resulting from sleep deprivation or sleep disorders is an important factor in the increasing number of accidents on today's roads. we describe a real-time safety prototype that controls the vehicle speed under driver fatigue. The purpose of such a model is to advance a system to detect fatigue symptoms in drivers and control the speed of vehicle to avoid accidents. In this paper, we propose a driver drowsiness detection system in which sensor like eye blink sensor are used for detecting drowsiness of driver .If the driver is found to have sleep, buzzer will start buzzing and then turns the vehicle ignition off . Once drowsiness is detected then buzzer will on and turns the vehicle ignition off . Then vehicle will stop immediately. Vehicle accidents are most common if the driving is inadequate. These happen on most factors if the driver is drowsy. Driver drowsiness is recognized as an important factor in the vehicle accidents. The National Sleep Foundation (NSF) reported that 51% of adult drivers had driven a vehicle while feeling drowsy and 17% had actually fallen asleep. Therefore real-time drowsiness monitoring is important to avoid traffic accidents. This involves controlling accident due to unconscious through Eye blink. Here one eye blink sensor is fixed in vehicle where if driver looses conscious and indicate through buzzer. The car simulator study was designed to collect physiological data for validation of this technology. Methodology for analysis of physiological data, independent assessment of driver drowsiness and development of drowsiness detection algorithm by means of sequential fitting and selection If the driver is found to have sleep, it warns and then turns the ignition off. And hence possibility of accident is avoided. 

The various advantages of the implemented system are mentioned below

• Detection of drowsiness

• Decreasing road accidents

• No need of monitoring
• cameras or other devicesare attached or aimed at the driver.

• This method is practically applicable.
• Automatic eye blink detection
• Helps avoid accidents
• Can be used in car, trucks, school bus, vans etc

the drowsiness of the driver is detected in the initial stage and the system alerts the driver. The location of the vehicle is update to the cloud. If there is any accident occurring, it immediately update the location of the accident to the ambulance server as Major or Minor accident. If the recovery switch is pressed within 60 secs of accident detection, it will be considered as minor accident. If the recovery switch is not pressed within 60 secs, it will be considered as major accident. By instantly updating the location, heartbeat and the level of drowsiness of the driver to the cloud as a black box, it make is easy to detect the cause of accident. However, for drowsiness detection high resolution and high frame per second recording camera is need for instant value calculation and detection. In future, in order to make the image more clear and to increase the speed of detection of drowsiness high frames per second camera should be used. To detect the drowsiness during night time, night vision IR based cameras should be used. Further, after drowsiness detection and alerting the driver, the acceleration system, the brake system and the gear system are controlled using the CAN network and the speed of the vehicle can be reduced gradually and the engine can be turned off. Ambulance stations can be implemented in more number of areas for quick response from the server.

This project is about making cars more intelligent and interactive which may notify or resist user under unacceptable conditions, they may provide critical information of real time situations to rescue or police or owner himself. Driver fatigue resulting from sleep deprivation or sleep disorders is an important factor in the increasing number of accidents on today's roads. we describe a real-time safety prototype that controls the vehicle speed under driver fatigue. The purpose of such a model is to advance a system to detect fatigue symptoms in drivers and control the speed of vehicle to avoid accidents. In this paper, we propose a driver drowsiness detection system in which sensor like eye blink sensor are used for detecting drowsiness of driver .If the driver is found to have sleep, buzzer will start buzzing and then turns the vehicle ignition off . Once drowsiness is detected then buzzer will on and turns the vehicle ignition off . Then vehicle will stop immediately. Vehicle accidents are most common if the driving is inadequate. These happen on most factors if the driver is drowsy. Driver drowsiness is recognized as an important factor in the vehicle accidents. The National Sleep Foundation (NSF) reported that 51% of adult drivers had driven a vehicle while feeling drowsy and 17% had actually fallen asleep. Therefore real-time drowsiness monitoring is important to avoid traffic accidents. This involves controlling accident due to unconscious through Eye blink. Here one eye blink sensor is fixed in vehicle where if driver looses conscious and indicate through buzzer. The car simulator study was designed to collect physiological data for validation of this technology. Methodology for analysis of physiological data, independent assessment of driver drowsiness and development of drowsiness detection algorithm by means of sequential fitting and selection If the driver is found to have sleep, it warns and then turns the ignition off. And hence possibility of accident is avoided.