Abstract: This project explores the integration of artificial intelligence (AI) techniques to augment driver detection systems in automotive environments, aiming to enhance overall road safety. The proposed system leverages advanced computer vision algorithms and machine learning models to accurately identify and monitor drivers in real-time. Key aspects include facial recognition, gaze tracking, and behavioural analysis to assess driver attentiveness and emotional states. The AI-assisted driver detection system contributes to proactive safety measures by providing timely alerts for potential driver distraction, fatigue, or impairment, detects weather the driver is drowsy, and also if the driver is continuously distracted even after limited number of alerts, our system will notify drivers superior or relative about repeated mistakes as a part of security. The project involves the development and evaluation of a prototype using diverse datasets and simulation scenarios to validate the system's effectiveness in various driving conditions. The outcomes offer valuable insights into the potential of AI in mitigating road accidents and improving overall transportation safety.
Keywords: Artificial Intelligence, Machine Learning, driver distraction detection, Computer Vision, facial recognition, gaze tracking, behavioural analysis.
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DOI:
10.17148/IJARCCE.2024.13515
