Enhanced Lane Detection System for Autonomous Vehicles Using Advanced Computer Vision

The increasing integration of artificial intelligence (AI) in automobiles has revolutionized vehicle security and access control. Traditional authentication methods, such as keys and PIN codes, are prone to theft and unauthorized usage, necessitating a more secure and intelligent solution. This paper presents an AI-driven driver face authentication system that leverages deep learning and computer vision to ensure secure and seamless vehicle access. The system captures and processes real-time facial images, matching them against a pre-registered driver database to authenticate the user before allowing vehicle operation. To enhance accuracy and reliability, the model incorporates advanced techniques such as convolutional neural networks (CNNs) for feature extraction and anti-spoofing mechanisms to prevent fraudulent access using images or videos. The system is designed to handle variations in lighting conditions, facial expressions, and partial occlusions, ensuring robustness in real-world scenarios. Additionally, its integration with in-vehicle telematics enables continuous monitoring and alerts in case of unauthorized access attempts. Experimental evaluations demonstrate high authentication accuracy with minimal false acceptance and rejection rates, making it a viable solution for modern automotive security. The proposed system not only enhances vehicle safety but also improves user convenience by eliminating the need for physical keys. By leveraging AI-powered biometric authentication, this research paves the way for secure, intelligent, and user-friendly automotive access control. Key Words: Autonomous Vehicles, Lane Detection, Deep Learning, Computer Vision, Convolutional Neural Networks (CNNs)