Image semantic segmentation is an important research field of computer vision and also one of the key technologies for scene understanding. In the field of unmanned driving, high-quality semantic segmentation of road scenes provides a guarantee for the safe driving of autonomous vehicles. First, this paper starts with the definition of semantic segmentation of road scenes and discusses the current challenges in this field. Second, this paper divides the semantic segmentation technology into a traditional segmentation technology, a traditional segmentation technology combined with deep learning and a segmentation technology based on deep learning, focuses on the semantic segmentation technology based on deep learning, and elaborates it according to three different network training methods of strong supervision, weak supervision and unsupervison. Then, the datasets and performance evaluation indicators related to the semantic segmentation of road scenes are summarized and compared, and the segmentation results using the common image semantic segmentation methods are analyzed. Finally, the challenges faced by the road scene semantic segmentation technologies and the future development direction are prospected.