Real-Time Road Scene Interpretation using Deep Learning in Driver Assistance Systems

Road segmentation enables vehicles to distinguish between drivable areas and the rest of the environment, which is crucial for autonomous driving and Advanced Driver Assistance Systems (ADAS). Incorrect segmentation is caused by conventional computer vision systems' failure to deal with complex road textures, varying lighting, and occlusions. This work proposes a road segmentation model based on deep learning that employs a U-Net architecture with skip connections and transposed convolution layers for high-resolution segmentation to address these challenges. Based on experimental results, the proposed approach enhances segmentation accuracy and reliability by attaining a validation accuracy of 85.6%. The findings enable intelligent transportation systems to enhance lane-keeping, real-time navigation, and overall driving safety. To further improve segmentation performance in challenging scenarios, next-generation work will focus on pairing transformer-based architectures with selfsupervised learning.