Innovative Framework for Distracted-Driving Alert System Based on Deep Learning

Abstract

Distracted driving is the most common cause of traffic accidents. According to a World Health Organization report, the number of traffic accidents has been increasing in recent years. To address this issue, distracted-driving recognition is an important area of traffic safety research. However, distracted behavior may be a part of a driver's regular tasks. For example, sometimes a delivery person must use his/her phone while driving. The use of walkie-talkies is required for container-truck drivers because they improve unloading efficiency and reduce the time cargo ships spend in a port, resulting in cost savings. While driving on the highway, it is sometimes necessary to tune the radio to receive an update on road conditions. Furthermore, drinking water is permitted while waiting for a traffic signal for an extended period of time. Therefore, for the distracted-driving alert system, the driving scenario is important. To address this issue, we present a novel framework herein that combines driving perception and driver behavior recognition to provide the driver with appropriate warnings. By combining driver perception and behavior recognition, our proposed framework can reduce false alerts. We also define different time-to-collision standards to achieve humane and effective warnings. We try to study various behaviors and define various time-to-collision standards for making safety-level decisions. For driving perception and driver behavior recognition, a modified convolutional neural network is used, which alerts the driver immediately.