In recent years, unstructured road segmentation has become one of the important research directions in the field of computer vision. Most existing methods are suitable for structured road segmentation and cannot meet the accuracy and real-time requirements of unstructured road segmentation. To address the above issues, this study improves the short-term dense concatenate (STDC) network by introducing residual connections to better integrate multi-scale semantic information. Additionally, it proposes a position attention-aware spatial pyramid pooling (PA-ASPP) module to enhance the network’s position awareness ability for specific regions such as roads. Experiments are conducted on two datasets, RUGD and RELLIS-3D, and the proposed method achieves a mean intersection over union (MIoU) of 50.78% and 49.96% on the test sets of the two datasets, respectively.