AlertDriver: a Real-Time Distraction Detection Solution

EasyChair Preprint no. 13441

Abstract

In today's modern age, transportation plays a crucial role in our daily routines, making road safety a top priority. Addressing this critical concern, we introduce AlertDriver—a real-time distraction detection system designed to significantly enhance driving safety by promptly identifying and addressing distracted driving behaviors. These distractions can include a range of activities such as using a mobile phone, drinking, or even falling asleep at the wheel.Through comprehensive evaluation using existing datasets, our experiments confirm the effectiveness of AlertDriver in accurately detecting and classifying instances of driver distraction. In essence, AlertDriver represents a proactive approach to improving road safety, harnessing advanced deep learning techniques for real-time distraction detection. With its capacity to swiftly identify and mitigate distracted driving behaviors, this solution holds great potential for reducing road accidents and ultimately saving lives.

Keyphrases: Alarm triggering, AlertDriver, computer vision, Distracted Driving, Distraction behaviors, driver behavior, Driver Safety, facial expressions, Real-time distraction detection, Yawning detection

@Booklet{EasyChair:13441,
  author = {S Annapoorna and Indu Pagadala and Mahitha Dondeti and Priyanvitha Bethapudi and Nithya Kaandru},
  title = {AlertDriver: a Real-Time Distraction Detection Solution},
  howpublished = {EasyChair Preprint no. 13441},

  year = {EasyChair, 2024}}