In order to solve the problems of vehicle autonomous positioning and navigation in the auto drive system， such as the inability to accurately estimate the body posture and the unsmooth navigation path， a positioning and navigation method based on a priori laser radar point cloud map was proposed. Using point cloud segmentation technology to separate feasible areas and potential risk sources， this paper studies the NDT （Normal Distribution Transform） point cloud registration and localization method based on optimized convergence process. The traditional A^* algorithm is improved from two aspects： dynamic weight design and domain first search strategy to meet the real-time positioning and navigation needs of autonomous driving. The experiment used Baidu Apollo Autonomous Driving Development Kit （D-KIT） for multiple control experiments. When the voxel downsampling Leafsize parameter was 1 （high sampling）， 1.2 （medium sampling）， and 1.5 （low sampling）， the localization time was reduced by 27.77%， 38.75%， and 38.30%， respectively. Four sets of navigation experiments were selected that meet the actual driving needs. After improvement， the maximum curvature of the navigation path was reduced by 80.9%， 74.9%， 65%， and 69.5%， respectively. The curvature of the navigation path remained low and stable， and the curvature data was consistent with vehicle dynamics. Provide effective methods for vehicle positioning and high-precision navigation.