## 3 Methodology
### Point Cloud Processing Algorithm
The previous module outputs the features of each point, and each point has a set of feature vectors. It reflects the semantic representation of each point in local and global contexts, but these features are specific to the point, not the whole point cloud. It can be aggregated into a single global feature after pooling. It is a high-level semantic representation of the entire point cloud, which represents the shape and structure of the integral point cloud and is no longer a point-by-point fine-grained feature. The inputs to the point cloud are permutation-invariant, and global pooling makes the model insensitive to the order in which the points are arranged. With the max pooling operation, the generated global features are always consistent regardless of the input order of the points. This eliminates the effect of point alignment. Max poolingtakes the maximum of all points for each feature dimension. This ensures robustness to noise and sparse data while effectively capturing the most significant geometric features in the point cloud. * Feature Dimension Expansion  
This step extends the feature dimension from 128 to 1024 using a linear layer, which improves global feature representation. Higher dimensionality allows for better representation of the complex geometric and semantic information of the point cloud. In complex tasks, point clouds may contain complex shapes and structures, and the higher the feature dimensions, the better the model can express these complex structures. More information capacity could capture more global patterns and semantics. The original PointNet algorithm has a fixed global feature dimension of 1024. The design has been experimentally validated and performs well in the task. Furthermore, this study uses the multi-head self-attention mechanism and multi-scale feature fusion, which could increase the richness of features. If the final global feature dimension is too low, it may limit the effectiveness of these modules. Extending to 1024 dimensions will allow the full potential of these modules to be utilized. The core idea of PointNet is to consider each point as an independent input, perform feature extraction on the point cloud using a multilayer perceptron, and then apply max pooling techniques to obtain global features. PointNet does not explicitly capture local relationships or spatial dependencies between points but rather obtains an overall feature representation through global pooling. The local feature extraction module introduced in this study is a direct improvement of PointNet, whereas PointNet treats each point independently, this module captures the local geometric features of the point cloud through neighborhood construction embedding. Such neighborhood aggregation introduces local structural information in the feature extraction process. In PointNet, global features are extracted from the features of all points by max pooling. In this model, the attention-based network mechanism enhances the ability of the model to capture global features. Through the self-attention mechanism, the model can dynamically focus on the relationships between points and assign higher weights to important features, thus effectively creating long-range dependencies in the point cloud data. In this study, after the extraction of global features, the final output features are obtained through global pooling. This step maintains the design concept of PointNet, which is to use global features for decision-making output. These improvements bring benefits to the model. First, by adding KNN and attention-based networks, the model is able to better understand the relationships between points, which is especially useful when passing through complex scenarios such as crowded intersections. In addition, the attention mechanism allows the model to dynamically adjust its focus to highlight the most relevant parts of the point cloud. This facilitates autonomous driving, where the importance of features changes with the environment.