## 4 Experiment and Result Analysis
### Quantitative Analysis
Figure 13: PTPR over PFPR scatter plots: (**a**) Key obstacle subclass; (**b**) Complete obstacle class. Figure 16: Scene 3 obstacle detection results. [PERSON] (2019) represents the Reference [19], [PERSON] (2011) represents the Reference [10]. Figure 15: Scene 2 obstacle detection results. [PERSON] (2019) represents the Reference [19], [PERSON] (2011) represents the Reference [10]. It can be seen from Figures 14 and 15 that there are two planes with different heights, the lawn and the road, in scene 1, and the horizontal road plane and the parking lot area with a certain inclination relative to the road plane appear in scene 2 (the area indicated by the yellow box in the left image). These two scenes are common uneven road scenes in urban road environments. In scene 1, reference [19] judged a large part of the free space as an obstacle, and there is an obvious misdetection. In scene 2, reference [10] misdetects most of the road surface of the parking lot as obstacles. The above situation occurs because these two methods rely on the accuracy of the road surface model, and if the road surface model cannot match the actual situation, it will have a negative impact on the detection performance. The OD-SPH method proposed in this paper also relies on an accurate road surface model, but it uses superpixel point clouds classified as free space by the OD-PNV method for road surface fitting. Therefore, the accuracy of the road surface fitting is improved, and the above-mentioned false detection situation is avoided. The false detection areas of OD-PNV are mainly concentrated near the hood of the vehicle, which does not consider the superpixel height feature, and is less affected by the uneven road surface. OD-LSF considers two kinds of spatial features comprehensively, and the false detection caused by the height feature error can be compensated by the plane normal vector feature. Therefore, the impact of an uneven road on OD-LSF is limited, and its detection results are the best in scene 1 and scene 2. Scene 3, shown in Figure 16, is a typical flat road scene in urban scenes. It can be seen from the figures that both reference [19] and the OD-SPH method detect obstacles close to the road as free space. This shows that even if the road model can correctly match the actual situation, it is difficult for these methods, which only rely on height features to correctly detect obstacles that are closer to the road surface. The false detection areas of the two methods of OD-PNV and OD-LSF are significantly smaller than that of the other three methods. However, it can be seen from the figure that the OD-PNV method has relatively more false detections in the hood and roof of the yellow vehicle (the area shown in the blue box in the figure). This is mainly because the area and the road surface have similar local plane normal vector features, so it is difficult to accurately distinguish them by only relying on the plane normal vector feature. The OD-LSF method makes up for the false detection caused by the plane normal vector feature through the height feature. The OD-LSF method proposed in this paper combines the advantages of the OD-PNV and OD-SPH methods. The detection effect has been significantly improved in the obstacle area close to the road surface and the vehicle hood and roof, and it can adapt to some uneven road scenes.