## 4 Experiment and Result Analysis
### Experimental Environment and Parameter Settings
#### 4.1.3 Dataset  
The semantic segmentation dataset of KITTI [33] is selected as the experimental dataset. This dataset has abundant images of urban traffic scenes, including a variety of obstacle types, and the corresponding semantic annotations are provided. The KITTI semantic dataset does not give the label of the test set, but only the label of the training set, so this experiment only uses the training set and divides it again. There are 200 images in the training set of the KITTI semantic dataset, 100 images of which are randomly selected as the training set for this experiment, and the other 100 images are used as the test set. The training set is used to draw the ROC curve and select the optimal parameters, and the test set is used to test the performance of each method under the optimal parameters.  
The semantic segmentation dataset of KITTI divides the pixels in the image into various classes according to the common objects in the traffic scene. However, as this paper only focuses on the obstacles and free spaces in the traffic scenes, the pedestrians, vehicles, buildings, and vegetation are classified into the obstacle class, while the roads, sidewalks and parking lots are classified into the free space class. Among the pixel classes defined in the dataset, the sky class and nature-terrain class are ignored in this paper. This is because the disparity of the sky is difficult to measure accurately, and the position of the sky has almost no effect on vehicle driving. Some nature-terrain is on the same plane as the road surface, while others are much higher than the road surface, so it is difficult to define whether the nature-terrain is free space or an obstacle. We performed a statistical analysis of the pixel number with different obstacle class labels in the dataset. The statistical result indicates that the pixel number of obstacle classes closely related to traffic behavior, such as vehicles, pedestrians and traffic signs, only account for 9.02% of the total pixel number, while the pixel number of obstacle classes far from the vehicle, such as buildings and vegetations, account for 50.71% of the total pixel number. Although the former accounts for a relatively small proportion, they are obviously more worthy of attention. If the two are not distinguished, the optimal parameters obtained from the ROC curves are mainly determined by obstacles such as buildings and vegetation. In order to make the algorithm pay more attention to obstacles such as vehicles, pedestrians and traffic signs, this paper divides a key obstacle subclass in the obstacle class and draws ROC curves with the key obstacle subclass of the training set to obtain the optimal parameters. In the quantitative evaluation of the performance of each method, the obstacles in the test set are, respectively, evaluated according to the key obstacle subclass and the complete obstacle class. In the following experiment, the key obstacle subclass includes pedestrians, vehicles and traffic signs, while buildings, trees, sky and nature-terrain are ignored. The complete obstacle class includes pedestrians, vehicles, traffic signs, buildings and trees, ignoring sky and nature-terrain.