STUDY AND ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR DETECTION OF DISTRACTED DRIVERS

The rise of Advanced Driver-Assistance Systems (ADAS) and Autonomous Vehicles (AVs) emphasizes the urgent need to combat distracted driving. This study introduces a fresh approach for improved detection of distracted drivers, combining a pre-trained Convolutional Neural Network (CNN) with a Bidirectional Long Short- Term Memory (BiLSTM) network. Our analysis utilizes both spatial and temporal features to examine a broad array of driver distractions. We demonstrate the advantage of this CNN-BiLSTM framework over conventional methods, achieving significant precision (up to 98.97%) on the combined ’Union Dataset,’ merging the Kaggle State Farm Dataset and AUC Distracted Driver Dataset (AUC-DDD). This research enhances safety in autonomous vehicles by providing a solid and flexible solution for everyday use. Our results mark considerable progress in accurately identifying driver distractions, pushing the boundaries of safety technology in AVs.