6 million – average number of car accidents every year. 3 million people are injured every year in car accidents in America. Around 2 million drivers experience permanent injuries every year from car accidents. 40,000 people lose their lives every year due to major accidents while driving. 40% of all deaths caused by car accidents involve alcohol. 30% of car accident fatalities are attributed to speeding. Reckless driving accounts for 33% of all deaths involving major car accidents.
Nearly 1 in 3 people surveys admitted to sending and or receiving text messages while driving. Distracted drivers are responsible for 1 in 5 injuries in auto accidents. Montana had the highest auto fatality rate in 2009 (2.0) Massachusetts had the lowest auto fatality rate in 2009 (0.6) 1996 was the least deadliest year for motorcycle riders. 69% of people surveyed admitted to talking on the phone while driving.
Driver distraction has become a critical area of study both for research in investigating human multitasking abilities and for practical purposes in developing and constraining new in-vehicle devices. This work utilizes an integrated model approach to predict driver distraction from a primarily cognitive secondary task. It integrates existing models for a sentence-span task and driving task and investigates two methods in which the resulting model can perform multitasking. Model predictions correspond well qualitatively to two of three measures of driver performance as collected in a recent empirical study.
Driver fatigue and distraction during travel are the major causes for the road accidents. Many driver monitoring systems have been proposed in recent years for monitoring driver activities to avoid accidents. Most of the existing systems are in the form of specialized embedded hardware, majorly present in luxurious vehicles. This paper presents an effective driver fatigue and distraction monitoring system for Android Automobiles. An intelligent system for monitoring driver fatigue and distraction during travel using Adaptive Template Matching and Adaptive Boosting is designed and implemented here. A novel approach of detecting eye rub due to irritation in eye and yawning detection through intensity sum of facial region is also proposed. Experiments are conducted using android OpenCV which can be installed in low cost smart phones as well as in Android Auto. Experiment results shows that a high accuracy of driver distraction is detected in different vehicles and camera locations.
SolutionDriver fatigue and distraction during travel are the major causes for the road accidents. Many driver monitoring systems have been proposed in recent years for monitoring driver activities to avoid accidents. Most of the existing systems are in the form of specialized embedded hardware, majorly present in luxurious vehicles. This paper presents an effective driver fatigue and distraction monitoring system for Android Automobiles. An intelligent system for monitoring driver fatigue and distraction during travel using Adaptive Template Matching and Adaptive Boosting is designed and implemented here. A novel approach of detecting eye rub due to irritation in eye and yawning detection through intensity sum of facial region is also proposed. Experiments are conducted using android OpenCV which can be installed in low cost smart phones as well as in Android Auto. Experiment results shows that a high accuracy of driver distraction is detected in different vehicles and camera locations.
Getting Started