Detection of In-car Driver Distraction Activities With Recommendations

Abstract

The increasing number of car accidents is a significant issue in current transpiration systems. th According to the world health organization (WHO), road accidents are the 8 highest top reason of death around the world. More than 80% of road accidents are caused by distracted driving, such as using a mobile phone, talking to passengers, and smoking. A lot of efforts have been made to tackle the problem of driver distraction; however, no optimal solution is provided. A practical approach to solving this problem is implementing quantitative measures for driver activities and designing a classification system that detects distracting actions. In this project, we have implemented a portfolio of various ensemble deep learning models that have been proven to efficiently classify driver distracted actions and provide an in-car recommendation to minimize the level of distractions and increase the level of in-care awareness for improved safety.