Automatic driving is one of the most difficult tasks in computer vision， and semantic segmentation in road scenes is one of the core technologies of automatic driving. This paper proposes an upsampling method based on enhanced semantic flow field， which can make the semantic information of the generated graph more detailed and the boundary clearer by learning the semantic flow field between adjacent feature graphs. At the same time， aiming at the difficulty of processing target scale changes and identifying small targets in road scenes， a new multi-level feature fusion method is proposed， which fully integrates deep semantic information and shallow detail information to adapt to targets of different scales. In this paper， CamVid is taken as the data set and data enhancement is carried out. Experiments show that both methods proposed in this paper bring significant improvement in accuracy. Compared with PSPNet， Deeplabv3+ and other models， the overall network has higher accuracy and the segmentation effect is closer to the real value.