Identifying passable areas is a crucial aspect of autonomous driving technology in mining. Open-pit mining road scenes are characterized by unclear road boundaries and varying surface flatness. When using traditional concentric circle ground segmentation models for fitting mining road planes, misclassification issues often arise, such as disconnection between passable areas and vehicles, and inconsistencies in passable area recognition results across frames. This paper proposed a method for identifying passable areas in mining roads based on spatiotemporal continuous compensation. First, the mining road was modeled using a concentric circle model, and principal component analysis was applied for multi-plane fitting to obtain the initial segmentation results of passable areas. Next, based on spatial connectivity, regional connectivity filtering and point connectivity filtering were performed on the initial passable areas using the region-growing algorithm and density-based spatial clustering of applications with noise algorithm, respectively, to obtain passable areas that meet spatial connectivity criteria. Finally, to eliminate unstable regions with inconsistent passability across different point cloud frames, a grid map was constructed based on a normal distribution transformation algorithm, and temporal stability weights were used to assess grid stability, ultimately filtering out unstable regions through regional grid projection. Test results in mining indicated that the proposed method for identifying passable areas achieved an accuracy of 93.44%, representing a 2.27% improvement over existing mainstream algorithms; the recall rate was 99.14%, reflecting an 8.26% enhancement compared to current mainstream algorithms. The proposed method not only exhibits good spatial connectivity in disconnected areas but also demonstrates strong temporal stability in rugged regions.