## 4 Dataset
### Network Model Test and Real Vehicle Experiments  
The research content of this section is to apply the neural network model to identify the drivable area in the test set and the actual scene and verify the feasibility and rationality of the network model. Test and real vehicle test results are shown in Figures 14-17.  
Figure 14 shows the detection effect of some test set data, including day and night, rain and snow environment. As seen in Figure 14a, the network model is able to identify the global drivable area. Figure 14b is the self-built test set of rain and snow, soil road, and other specific circumstances. The original data frame has no obvious pavement structure, and the features are relatively broken. The network model can still identify the target area to a certain extent, and the area division is close to the actual situation.  
There are two reasons for the decreased detection effect in Figure 14b compared to (a). First, there is no obvious feature for network detection after the road surface is covered with snow, and there is snow on the boundary between the road and the area alongside the stone. The boundary between the two is blurred and disappeared, resulting in misjudment. Second, there is a shortage of strongly correlated data in the dataset, and there are situations in which the correlation feature mapping is attributed to the background class during the learning process, causing the network to fully train for specific situations. For the latter reason, this paper uses the network model trained by the cross-country environment drivable area dataset for simple testing. Considering the unevenness of the road surface and the spatial information of some elements, the grass category is used as the drivable area for training. The model and the results are shown in Figure 15. It is shown that the detection effect has a relatively obvious improvement, but there is also a misjudment of the vertical spatial structure to a certain extent, which shows the influence of the dataset structure on the generalization ability of the network model. It also shows that some of the data in the self-built test set of this paper are close to the off-road environment and need further improvement. In addition, it shows that the network model of this paper has a certain generalization ability after being trained by suitable datasets.  
Figures 16 and 17 show the intercepted frames of the real-time detection effect of the vehicle driving process under day and night conditions, respectively, which are intercepted according to the scene change amplitude to reflect the algorithm effect and not according to the fixed number of frames.  
The training, testing, and real-vehicle experimental results demonstrate that the proposed network structure can effectively complete drivable area detection under varying conditions. It delivers robust performance on diverse data in the dataset. This indicates good generalization ability, feasibility, and applicability.  
Figure 14: Single-frame experimental effect of test set: (**a**) training effect of partial test set and (**b**) self-built test set that includes rain, snow, soil road, and other specific circumstances.  
Figure 15: Effect of cross-country dataset training model test set.