## 3 Methodology
### Point Cloud Processing Algorithm  
As the first deep learning architecture to directly process 3D point cloud data, PointNet has a simple and efficient architecture, an end-to-end feature learning capability, and adaptability to irregular point cloud data [31]. However, while it performs well in global feature extraction, it has limitations in capturing local and global dependencies within the point cloud. PointNet processes each point independently and then obtains global features through max pooling. This design is unable to adequately account for the relationships between points when faced with complex 3D structures, especially in capturing long-range dependencies and local details. In future autonomous driving tasks, agents may encounter highly complex environments and diverse obstacles; the proposed technological approach should enhance scalability and generalizability to address these challenges. The system not only needs to understand the overall structure of the surrounding environment but also needs to capture the intricate details and relationships between objects at different distances to ensure that the task can be performed safely and effectively. Therefore, data preprocessing algorithms for sensor inputs must be capable of addressing this challenge. The attention mechanism provides a flexible and effective way to improve this aspect [32]. By strengthening local feature capture between point clouds and improving fine-grained information modeling, the approach enhances the model's understanding of global structures and complex dependencies within point clouds. This improvement draws inspiration from the successful application of Transformer architectures in natural language processing. To boost point cloud data processing capabilities, the research introduces self-attention mechanisms to enhance the model's local feature capture, rendering it more accurate and robust when processing irregular and complex three-dimensional point cloud scenarios. The workflow of the point cloud processing stage used in this study is shown in Figure 2 and summarized below. Figure 1: DDPG workflow. Point cloud input  
Three-dimensional LiDAR acquires the environment information and inputs the point cloud. The point cloud data used in this study only take the 3D coordinates of each point without other information such as color. The data format is N \(\times\) 3, i.e., 3D coordinates of N points (x, y, z). This module applies Multi-Layer Perceptron (MLP) to each point independently. It preserves the original design of PointNet. The 3D point coordinates are embedded into the high-dimensional feature space to provide a richer representation for subsequent feature extraction. The difference is that the dimension of the PointNet output is 64, and this study extends the dimensions to 128. It can provide more information for subsequent local and global feature modeling. The main task of the original PointNet is point cloud classification and simple semantic segmentation. Sixty-four-dimensional features are sufficient for capturing local features. The design of PointNet focuses on global max-pooling, which integrates point cloud features at the global feature level. However, this study introduces multi-head self-attention mechanisms in the subsequent steps, which usually require high input feature dimensions. A higher initial feature dimension allows the attention module to establish point-to-point relationships more efficiently. In the feature extraction module, multi-head attention usually requires high input feature dimensions in order to compute the matrix multiplication of query, key, and value. If the initial feature dimensions are too low, subsequent modules will not have enough information to compute the attention. The output dimensions of the initial MLP are consistent with the local and global attention modules, avoiding additional dimension expansion or contraction operations.