RESEARCH ON APPLYING DEEP LEARNING NETWORKS IN LANE DETECTION FOR SELF-DRIVING VEHICLE SYSTEMS

Abstract

Automatic lane detection technology plays a crucial role in enabling self-driving cars to accurately position themselves in multi-lane urban traffic environments. However, recognizing lane markings under different weather conditions remains a significant challenge for traditional image processing methods and computer vision techniques. This study makes a substantial contribution by applying deep learning networks to address the problem of lane detection in autonomous vehicle systems. Specifically, the authors employed an Encoder-Decoder model with a fully convolutional network to effectively detect lanes. The research and experimental processes were conducted rigorously, yielding impressive results with an accuracy of up to 97.82%.