## 4 Experiment and Result Analysis
### Analysis of Results  
To evaluate the detection effect of our fusion method (PF-SSUn) for unexpected obstacles, PF-SSUn was compared with the following three methods: (1) Stixels [4]: It is a classic obstacle detection method based on stereo vision, which can describe the objects with multi-layer stixels; (2) UOD-SS (Unet): The semantic segmentation model DeepLabV3+ of UOD-SS was replaced by Unet to compare the ability of these two semantic segmentation models to detect unexpected obstacles; (3) UOD-SS. Table 1 lists the performance indicators of PF-SSUn and the above three methods. As can be seen from the table, the Stixels method has the lowest detection rate and the highest false-positive rate, and the detection effect is far worse than the other three methods. This shows that the Stixels method based on stereo vision has obvious disadvantages in the detection of unexpected obstacles compared to the semantic segmentation method. This is mainly due to the fact that stereo vision methods are much more affected by the sizes and shapes of obstacles than semantic segmentation methods. UOD-SS (Unet) is worse than UOD-SS in both detection rate and false-positive rate indicators, and it is difficult to meet traffic safety requirements. UOD-SS has better performance indicators, which is mainly due to the ASPP structure of DeepLabV3+ and the introduction of atrous convolution to improve its detection ability. Compared with UOD-SS, PF-SSUn significantly improves the detection rate of unexpected obstacles. Although the false-positive rate also increased to a certain extent, considering the improvement in detection rate, the cost is acceptable. Figure 7: Image examples of datasets: (**a**) the Lost and Found dataset; (**b**) the Cityscapes dataset. From a safety point of view, to a certain extent, we pay more attention to the improvement of the detection rate. Therefore, two scenes in the Lost and Found dataset are selected to test the frame-by-frame detection rate of the above methods, and the results are shown in Figure 8 (the distance of obstacles is from far to near). Since the Stixels method has significantly the lowest detection performance, only three other semantically relevant methods are shown here. It can be seen that the detection rates of the three methods are relatively high at close range, and the performance is stable. However, the detection capability of PF-SSUn has been significantly improved at long distances. Especially in scene 2, which is more difficult to detect, UOD-SS (Unet) can hardly detect long-distances unexpected obstacles, and the detection rate of UOD-SS is also significantly lower, but PF-SSUn has always maintained a stable and relatively high detection rate. This is mainly because it is very difficult to detect unexpected obstacles at a long distance. PF-SSUn makes up for the detection omission of UOD-SS and improves the detection rate by probability fusion of UOD-SS and UOD-Un. Figure 9 shows the PDR over PFPR scatter plots for the three methods in the two scenes. A high detection rate and low false-positive rate (upper left corner of the figure) can reflect the best detection performance. As shown in the figure, the marked points of PF-SSUn are closer to the upper part of the figure than the other two methods, and only a few points are located on the right side of the figure due to the increase in the false-positive rate, but their values are always very small (less than 1%). Due to the low detection rate of UOD-SS (Unet), many points appear at the bottom of the figure. The marked points of UOD-SS are closer to the upper part of the figure than UOD-SS (Unet), but there are still many points scattered in the middle of the figure.