## 4 Experiment and Result Analysis
### Analysis of Results
To evaluate the detection effect of different fusion strategies, PF-SSUn was compared with the following four methods: (1) UOD-SS; (2) UOD-Un; (3) Fusion-And: Both methods detect a pixel as an unexpected obstacle, then it is determined that the pixel is an unexpected obstacle; (4) Fusion-Or: At least one method detects the pixel as an unexpected obstacle, and the pixel is determined to be an unexpected obstacle. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **PDR (\%)** & **PFPR (\%)** \\ \hline Stixels & 52.41 & 4.11 \\ UOD-SS(Unet) & 63.23 & 0.45 \\ UOD-SS & 83.58 & 0.30 \\ PF-SSUn & 92.33 & 0.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of performance indicators of the four detection methods. Figure 8: Comparison of frame-by-frame detection rate of the three semantically relevant methods: (a) Scene 1; (b) Scene 2. Table 2 lists the performance indicators of PF-SSUn and the above four methods. It can be seen from the table that the detection rate of both independent methods is lower than that of PF-SSUn. When used as an independent method to detect unexpected obstacles, UOD-Un performs unsatisfactorily in detection rate and false-positive rate. However, because UOD-Un uses a completely different detection method from UOD-SS, and the uncertainty information it uses is quite different from semantic information, it plays a good supplementary role to the latter. In terms of fusion strategy, Fusion-And focuses on reducing the false-positive rate, so it achieves the lowest false-positive rate, but the detection rate indicator is very poor. Fusion-Or achieves the highest detection rate, but it also has the highest false-positive rate. The detection rate of PF-SSUn also achieves very good results (only slightly lower than Fusion-Or), but the false-positive rate is only one-ninth of Fusion-Or. This shows that our fusion strategy maintains a relatively low false-positive rate while focusing on improving the detection rate. Figure 10 shows the scatter plots of PDR over PFPR for different fusion strategies in two scenes. As can be seen from the figure, the points of PF-SSUn are concentrated in the upper left area, while the points of Fusion-And and Fusion-Or produce downward and right deviations, respectively. \begin{table}
\begin{tabular}{c c c} \hline
**Method** & **PDR (\%)** & **PFPR (\%)** \\ \hline UOD-SS & 83.58 & 0.30 \\ UOD-Un & 62.43 & 3.17 \\ Fusion-And & 52.69 & 0.08 \\ Fusion-Or & 93.32 & 3.39 \\ PF-SSUn & 92.33 & 0.37 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of performance indicators between independent method before fusion and different fusion strategies. Figure 10: PDR over PFPR scatter plot of the three fusion strategies: (**a**) Scene 1; (**b**) Scene 2. Figure 9: PDR over PFPR scatter plot of the three semantically relevant methods: (**a**) Scene 1; (**b**) Scene 2. Figure 11 shows the comparison between the detection results of the three methods proposed in this paper with the ground truth on three images in the test set. These three images are selected because they have great differences in scene, lighting, and appearance of unexpected obstacles, and the distance is very long. The detection results of the last row are obtained by the general semantic segmentation in Section 3.2 and the superposition of PF-SSUn results. The unexpected obstacle in the image on the left column is a toy car. It can be seen from the enlarged image that UOD-SS has detected a part of the toy car, while UOD-Un performs better. The fusion method (PF-SSUn) embodies the advantages of UOD-Un in this unexpected obstacle detection. The middle column image contains three unexpected obstacles. UOD-SS misses many areas due to the long distance of obstacles. Although there are more false-positive pixels in the detection results of UOD-Un, it detects most of the unexpected obstacle areas. The detection results after fusion cover most areas of unexpected obstacles, and the false-positive pixels are significantly reduced compared to UOD-Un.