The LIDAR point cloud ground segmentation algorithm in the autonomous driving sensing module has low segmentation accuracy that requires further improvement. To address this problem， a ground point cloud segmentation algorithm is proposed based on a seed point distance threshold and road fluctuation weighted amplitude adaptive approach. Firstly， the method establishes a correlation between the selection threshold of seed points and the horizontal distance feature of the two-dimensional plane based on polar coordinate raster map division and controls the update of the seed point set through the change in horizontal distance between point clouds. Subsequently， in the process of road model fitting， the slope continuity judgment criterion is introduced to solve the stagnation problem of the slope pavement model update. Finally， the segmentation threshold equation of point clouds is established according to the change in the weighted amplitude of road surface fluctuation. This enables the achievement of adaptive threshold segmentation with respect to the distance feature of point clouds. In this paper， point cloud binary classification data processing on the open-source dataset Semantic KITTI is performed， and the performance of the algorithm is tested. The experimental results demonstrate that the ground segmentation algorithm described in this paper exhibits an improvement of 2%-4% in precision and recall when compared to existing algorithms. This substantiates the high accuracy of the algorithm proposed in this study.