## 4 Dataset  
The following four datasets are constructed and used in the following experiments.  
1. The global drivable area dataset includes 8000 instances of the BDD100k [46] dataset and 16,063 instances of the IDD dataset [47]. The road, parking, and ground annotations in the BDD100k dataset and the road, parking, and drivable fallback annotations in the IDD dataset are used as the target drivable areas, and the corresponding conversion masks are generated. The scene includes scenes of multiple pedestrians, vehicles, and obstacles in cities and villages, as well as lane-less roads, paved roads, and unpaved roads.
2. The road drivable area dataset contains 80,000 copies of BDD100k dataset data and corresponding conversion masks. This dataset mask file is mainly generated based on the area/drivable kinds and area/alternative kinds in the annotation of the BDD100k dataset, which can be used as a control group of unstructured environments to verify the applicability of the model structure of this paper in a variety of road situations.
3. The off-road environment drivable dataset, containing 7436 data from the RUGD dataset [48], and the corresponding transformation masks validate the generalizability of the model in this paper in the face of somewhat challenging data situations.  
4. Self-built small datasets are used to simply evaluate the applicability of this paper's model in the Chinese environment and also participate in the training and testing of this paper's network model together with the validation and test sets in the above (1) and (2) datasets.  
Since most public datasets consist of scenes from other countries that are unrepresentative of Chinese environments, we collected information and data using industrial cameras, smart handheld devices, and other equipment across various regions in China. The data cover diverse conditions including residential areas, campus roads, urban-rural junctions, day and night, clear and inclement weather, and different times of day and seasons. The data format includes continuous videos and individual single-frame pictures, covering old lane-free, unmarked highways, community unstructured roads, rural dirt roads, night unmarked highways, rain and snow-covered roads, etc. At the same time, network data collection is carried out to supplement the self-collected data set. Part of the acquisition range is shown in Changchun, China, in Figure 6. Compared with the publicly available dataset, the dataset collected in this paper has some quantitative gaps. Sample self-picked datasets are shown in Figure 7. Therefore, this paper randomly selects some images in the self-built dataset for annotation.  
includes an Nvidia GTX 1660 Ti GPU, 16 GB DDR4 memory, and an AMD Ryzen 5800H 3.20 GHz CPU.  
In this paper, we utilize an industrial camera with enhanced manufacturing accuracy and quality control as the visual sensor, along with an appropriate lens. The industrial camera is a Hikvision area-array CMOS camera, model MV-CE013-50 GC and they are shown in Figure 8.  
The vehicles used for data collection and real-world testing are Dongfeng Nissan Qichen R50 and Wuling Hongguang MINIEV. Figure 9 and Table 2 show the experimental vehicle and corresponding parameters, respectively. To enable convenient sensor installation while minimizing the effects of low temperature, the camera was horizontally mounted below the windshield, aligned with the vehicle's center axis and placed in the center console.