## 2 Related Work  
Deep learning has considerable performance in computer vision, especially the convolutional neural network, which is widely used in computer vision. Since 2012, AlexNet [1] had good performance in the ImageNet Image Recognition Challenge in 2012, various convolutional neural networks have been created to achieve better performance, such as the ResNet series [2; 3; 4] and NASNet series [5; 6; 7; 8]. Additionally, the MobileNet series [9; 10; 11], ShuffleNet series [12; 13], and EfficientNet series [14; 15] which have outstanding performance while being lightweight. The field of image recognition is also the first field to use convolutional neural networks.  
However, image recognition is not the most accurate method of recognition. In order to further mark the objects to be recognized on the pixels of the image, semantic segmentation was developed. Since FCN [16] performed well in the field of semantic segmentation in 2014, many methods have been created in the field of deep convolutional networks in semantic segmentation. Since SegNet [17] was proposed, which first used an encode-decode structure, the UNet series [18; 19; 20], which has outstanding performance in medical imaging and recent application in the field of image semantic segmentation, and 3D LiDAR semantic segmentation, transformers [21; 22; 23] all use the encode-decode structure.  
In addition to the encode-decode structure, PSPNet [24], which is known for its pyramid pooling, also has had a profound impact on the development of semantic segmentation. DenseASPP [25], which has atrous convolution, is also a pyramid pooling structure, and the DeepLab series [26; 27; 28; 29] have been inspired by the encode-decode structure and the pyramid pooling structure.  
Road semantic segmentation is a crucial part in the field of autonomous driving. There are two main methods for road semantic segmentation. The first method is the pure image-based method [30; 31; 32; 33; 34; 35] which is commonly used. In order to further improve the accuracy, the multi-sensor fusion method [37; 38; 39; 40; 41; 42] is also a commonly used method for drivable space detection. SNe-RoadSeg+ [43] uses a combination of depth images and RGB images, adding more depth information to improve the detection effect. However, compared with LiDAR, LiDAR has a higher stability and better robustness to solve various situations. Therefore, LiDAR and image fusion methods, such as PLARD+ [44], are also commonly used methods for drivable detection. However, the algorithm of multi-sensor fusion is more complicated. Different sensors, different receiving ranges, different receiving speeds, and the amount of data that the computing unit can process at a time is also different, and the calibration is also more complicated. The equipment is also more expensive, and it needs to be able to process data from different sensors, which also makes an amount of calculations. In order to solve this problem, this paper proposes a simple but effective convolutional neural network named Light-Weight Fast VGG (LWF-VGG) that modified VGG16 [45] to detect the drivable space of autonomous driving vehicle. Additionally, in order to be able to cope with the effect of different time and climate on the road image, this paper proposes a model-switching architecture to solve various road conditions.