Precision in Motion: enhancing autonomous driving with advanced Lane Recognition using High Resolution Network

Abstract

Autonomous cars are revolutionizing transportation by navigating roadways without human intervention using digital technology and artificial intelligence. However, reliable lane recognition is a big barrier in this endeavor. Lane identification is a complex topic that presents significant challenges to computer vision and machine learning systems. Accurate lane line detection can be challenging due to real-world driving conditions, resulting in negatively impacts steering angle prediction. In response to this difficulty, our research proposes a novel strategy to improving lane detection and steering control accuracy. To recognize lanes with better precision, we use computer vision techniques, namely semantic segmentation. Semantic segmentation allows the vehicle's internal artificial intelligence system to classify each pixel in an image as belonging to a given object class, such as road lanes. The precise lane detection required for secure and dependable navigation is addressed by this suggested methodology, which addresses a crucial part of autonomous driving technology. In this paper we have improved the accuracy and robustness of autonomous vehicles, preparing them to face the difficulties of real-world road conditions, by using HR-Net architecture.