DRIVER ACTIVITY RECOGNITION BY DRIVER PROFILES USING DEEP LEARNING

Abstract

1. This study presents a novel approach to driver activity recognition utilizing deep learning techniques 
tailored to diverse driver profiles. With the increasing integration of advanced driver-assistance systems 
(ADAS) in vehicles, the accurate detection of driver behaviors is critical for enhancing road safety and 
optimizing user experience. We collected a comprehensive dataset from various drivers, capturing a wide 
range of activities, including normal driving, distraction, aggressive maneuvers, and periods of inactivity. 
2. Leveraging deep neural networks, particularly Long Short-Term Memory (LSTM) and Convolutional 
Neural Networks (CNN), we developed a robust framework that analyzes sensor data and video inputs to 
recognize and classify driving activities. Our model incorporates feature extraction techniques to identify 
key behavioral patterns across different driver demographics, ensuring generalizability and reducing bias. 
3. Extensive evaluation metrics, including accuracy, F1 score, and confusion matrices, demonstrate the 
model's effectiveness in real-time scenarios. The findings indicate significant potential for improving safety 
systems, informing insurance models, and enhancing driver training programs. This  
4. research underscores the importance of considering diverse driver profiles in developing intelligent 
transportation solutions.