## 2 Related Work
For this reason, some references have introduced more complex road surface profile models, such as clothoid [23] and B-spline surface [24]. However, because the road surface usually presents many local features (such as local protrusions and depressions), it is difficult to use a unified model to describe it. As a result, the detection effect of the above methods largely depends on the accuracy of the road surface parameters and the effectiveness of the models. Another research idea is to detect obstacle points through the geometric relationship between 3D point pairs. For example, Professor [PERSON] et al. [25] proposed a position-based object segmentation method using the 3D reconstruction capability of stereo vision. This method performed obstacle segmentation based on object depth in an aerial view. [PERSON] et al. [26] analyzed image sequences from the spatial and temporal perspectives. They combined stereo vision and image tracking features to compute 3D point clouds to represent obstacles. In reference [27], instead of using all of the 3D point cloud data, some 3D point cloud candidates extracted by height analysis were used for obstacle detection, which improved the detection accuracy in specific scenes. Reference [28] fused the point cloud of lidar and the image of the camera, obtained the point cloud belonging to the obstacle by using the u-disparity map, and then used the clustering algorithm to segment and locate the obstacles. The images generated by these methods are pixel-level obstacle representations. As there is no need to model the road surface, the detection effect is less affected by the road conditions. Reference [6] proposed a framework of a superpixel-based vehicle detection method using a plane normal vector in the disparity space, which used lane estimation to set the ROI to reduce the amount of calculation. Then, the road plane was fitted by the K-means algorithm, and the candidate areas of the road and obstacles were extracted. Finally, HOG and SVM were used as features and classifiers to verify obstacle candidate areas, respectively. This method took advantage of the geometric relationship between 3D point cloud, but it was still limited by the richness of the data set and the application scenes were limited. However, these methods all need to cluster 3D point clouds. In complex traffic scenes, the uncertainty of the clustering results leads to an unstable detection performance, which is difficult to promote in demanding applications. In fact, the 3D point clouds contain rich spatial information, and the points in the scene are usually not isolated. The local spatial features of the point cloud can just reflect the connection between the spatial points. Aiming at the complex problem of obstacle detection in traffic scenes, this paper designs an obstacle detection method with a high detection rate and strong stability. This method comprehensively considers the probability of existing obstacles in the local area from the perspective of the plane normal vector feature and superpixel point-cloud height feature. As the spatial information used by these two features is different, the fusion of the two features achieves good results.