Semantic Segmentation for Autonomous Driving

Abstract

The task of semantic segmentation involves labeling each pixel in an image with its corresponding object class, which is achieved by clustering regions belonging to the same category using artificial intelligence. This is an important step from image processing to image analysis and has numerous applications in areas such as automatic driving, image enhancement, and 3D map reconstruction. With the emergence of deep learning, several sophisticated and efficient algorithms have been developed for this task. This chapter aims to review these methods, starting with a discussion of state-of-the-art semantic segmentation methods for both single modality and data fusion, emphasizing their contributions and significance in the field. Additionally, an overview of commonly used datasets is provided to assist researchers in selecting the appropriate dataset for their needs and goals. A comprehensive summary of evaluation metrics used to assess semantic segmentation results, along with corresponding benchmarks for a number of classic datasets, is also presented. Finally, practical applications of semantic segmentation in autonomous driving are explored, and conclusions are drawn on the current state of the art.