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

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