Blind Spot and Drowsiness Detection for Intelligent Transportation System

Abstract

This research paper investigates two critical aspects of Advanced Driver Assistance Systems (ADAS): Blind Spot Detection (BSD) and Drowsiness Detection (DD). Our primary objective is to assess these ADAS features, emphasizing their efficacy, advantages, and constraints. SD relies on sensors and cameras to monitor blind spots, alerting drivers to potential lane change risks. DD employs sensors and algorithms to detect signs of driver drowsiness or fatigue, issuing timely warnings. This study analyzes technological innovations in BSD and DD, examining their integration into vehicles and their potential to mitigate accidents caused by blind spots and drowsy driving. We also explore human factors, including user acceptance and usability concerns. Additionally, we assess the real-world impact of BSD and DD on road safety, utilizing data from studies and accident reports to quantify their effectiveness. This research contributes to discussions on ADAS technologies, emphasizing their role in enhancing road safety. We aim to encourage further development and integration of these systems into the automotive industry, ultimately leading to safer transportation for all.