# Reinforcement Learning Decision-Making for Autonomous Vehicles Based on Semantic Segmentation
## 2 Materials and Methods  
### Semantic Segmentation Network  
Semantic segmentation classified each pixel in an image to separate and label scene elements such as roads, traffic signs, pedestrians, and obstacles. This provided critical semantic information for the decision-making system to identify drivable areas and potential hazards. With the development of Convolutional Neural Networks (CNN), semantic segmentation models based on fully convolutional networks achieved high segmentation performance. The semantic segmentation model used in this paper was DeepLabv3P [17].  
Compared to other FCN-based methods, DeepLabv3P uses Atrous convolution and the ASPP module, providing better multi-scale feature extraction capabilities. This is crucial for capturing small objects (such as pedestrians) and large background structures (such as road boundaries), making it highly suitable for the diversity and dynamic environments of autonomous driving. DeepLabv3P includes an additional decoder that enhances edge detail preservation and improves the segmentation of small objects and fine boundaries. This is especially beneficial for tasks such as recognizing drivable areas and obstacles. DeepLabv3P demonstrates better generalization capabilities in handling complex scenes, making it more reliable in dynamic traffic environments. The DeepLabv3P network processes input images through stacked convolutional layers and atrous convolutions, ultimately producing a pixel-level prediction map of the same size as the input image. Its overall structure is shown in Figure 1.  
The Encoder consisted of a backbone network (DCNN) with atrous convolutions, and its backbone structure used an improved Xception model. Following it was the Atrous Spatial Pyramid Pooling (ASPP) module, which introduced multi-scale information. The network structure also included a Decoder module that further fused feature maps to optimize segmentation performance, particularly at the edges of targets. Additionally, the DeepLabv3P network applied Depthwise Separable Convolution to the ASPP and Decoder modules, improving the stability and efficiency of semantic segmentation. The Encoder provided two inputs to the Decoder: the first was feature maps obtained through the ASPP module and processed by an additional convolutional layer; the second was low-level features.  
The Decoder received two outputs from the Encoder. It applied a \(1\times 1\) convolution to the input low-level features for channel reduction. The feature maps from the ASPP were resized through interpolation to match the dimensions of the low-level features and then concatenated with the channel-reduced low-level features. The combined features were then processed by a set of \(3\times 3\) convolutional layers. Finally, linear interpolation upsampling was used to generate a prediction label map with the same size as the original image.  
The input images for training the semantic segmentation model were 512 \(\times\) 512 pixels. The model was trained using an SGD optimizer with an initial learning rate of 0.01 and a polynomial decay scheduler with a batch size of 4. The training was carried out for 70,000 iterations. The inference speed of the DeepLabv3P model on 512 \(\times\) 512 input was 50 ms per frame.