Driver Assistance System using Raspberry Pi and Haar Cascade Classifiers

Around 43% of road accidents are due to drowsiness of a driver, says a study by the Central Road Research Institute (CRRI). Another leading cause for road accidents is drunken driving. Any amount of alcohol can impact a person's driving ability and slows their response time. On an average 8 people die every day because of driving under the influence of alcohol. In case of an accident to reduce the fatalities and get quick emergency response a vehicle crash detection mechanism is necessary. Road accidents claim nearly three lives every minute, so it is of utmost importance to develop a cost-efficient driver assistance system for automobiles. This will help us to monitor the driver's physiological behaviors which will affect the stability of the vehicle and avoid accidents. To implement this, a variety of software algorithms, input and output extraction hardware tools have been employed in a collaborative way. The system developed consists of three interconnected modules namely, driver drowsiness detection using Haar cascade classifier and OpenCV, followed by alcohol content detection using MQ3 sensor and lastly the accident/crash detection using Piezoelectric sensor. To detect the onset of fatigue or loss of vigilance of the driver, within the close vicinity of the driver multiple sensors are embedded on this prototype.