Novel view synthesis and realistic scene reconstruction is crucial for advancing data augmentation of autonomous driving systems. However, previous 3D Gaussian Splatting methods excel in rendering with similar viewpoints, but cannot handle significant viewpoint changes, while some methods introduce additional information to improve rendering capability at the cost of increased computational overhead. To address it, we propose the Gaussian splatting that uses the Motion Trajectory priors (GMT) method, a specialized tuning of 3D Gaussian Splatting aimed at novel view synthesis of road regions under sparse-view conditions. Our method applies geometric constraints to the 3DGS that represent the road regions using only the ego-motion trajectory as a prior, without introducing additional information, which saves the computational overhead. We achieve multi-lane simulation on Waymo [15] and KITTI [5] at a horizontal displacement of 3 m, our module optimization has increased FID by 20.6 points.Our approach applies geometric constraints to the Gaussians that represent the road regions.