A Survey on Deep Learning Based Approaches for Scene Understanding in Autonomous Driving

As a prerequisite for autonomous driving, scene understanding has attracted extensive research. With the rise of the convolutional neural network (CNN)-based deep learning technique, research on scene understanding has achieved significant progress. This paper aims to provide a comprehensive survey of deep learning-based approaches for scene understanding in autonomous driving. We categorize these works into four work streams, including object detection, full scene semantic segmentation, instance segmentation, and lane line segmentation. We discuss and analyze these works according to their characteristics, advantages and disadvantages, and basic frameworks. We also summarize the benchmark datasets and evaluation criteria used in the research community and make a performance comparison of some of the latest works. Lastly, we summarize the review work and provide a discussion on the future challenges of the research domain.

Automated summary

This paper provides a comprehensive survey of deep learning-based approaches for scene understanding in autonomous driving, categorizing them into four work streams and analyzing their characteristics, advantages, and disadvantages. It also summarizes benchmark datasets and evaluation criteria used in the research community, compares the performance of some latest works, and discusses future challenges in the research domain.