## 3 Method  
### Environmental Perception Attention (EPA)  
In real driving, the model needs to have the ability to \"see\" a wide range of environmental information to help accurately and effectively locate and identify various targets. When faced with complex scenes, the model can more effectively identify key information, reduce interference and noise, and automatically select the features most relevant to the current decision. In addition, the model must also adapt to different weather conditions (such as rain, snow, fog, etc.) and lighting environments (such as direct sunlight or night driving) to enhance its robustness and reduce the misjudgment rate. Especially when identifying distant obstacles, pedestrians, and other vehicles, it is particularly important for the model to focus on a wide area around the vehicle. We designed a lightweight environmental perception attention (EPA) to help the model capture long-range contextual relationships, supplement a wide range of environmental information into the network model, and improve the robustness of the model in various scenarios. The environmental perception attention structure is shown in Figure 1.  
Specifically, given an input feature map \(X\in\mathbb{R}^{H\times W\times C}\), where \(C\) is the number of feature map channels, \(H\) and \(W\) are the height and width of the feature map space dimensions,respectively. The calculation method of the convolution operator aggregates local range context information onto the new feature map, which can be expressed as  
\[f_{h,w}=\sum_{h,w}W_{k,k}^{\mathrm{C}}*F_{k,k}^{\mathrm{C}} \tag{1}\]  
where \(k\) is the convolution kernel size, f is the output feature map, \(W\) is the convolution kernel weight parameter, and \(F\) is the input feature map. If \(k\) is increased to \(h\) or \(w\), although the global receptive field is obtained, it will introduce quadratic computational complexity, that is, \(O(h^{2}w^{2})\), which is particularly obvious in high-resolution scenarios [43]. Considering the lightweight and efficient nature of the attention mechanism, it is required to preserve long-range dependency information and obtain the attention weight results in a simple and effective manner. We decompose it by rows and columns, obtain the relationship between the long-distance row and column spatial positions, and effectively enhance the interactive ability of positioning information embedding through the interaction of feature information extracted by rows and columns. From the spatial dimension, the decomposition operation aggregates wide or high feature information in a fully connected manner. After the row is fully connected, each position of the output feature map is aggregated by the corresponding row information of the input, and then the corresponding column information is aggregated through the column fully connected, thereby indirectly capturing long-distance position information, and the computational overhead is reduced to \(O\big{(}h^{2}w+hw^{2}\big{)}\). From the perspective of convolution operations, the above steps are to decompose the two-dimensional convolution into rows and columns and perform one-dimensional convolution operations, respectively, which is also a popular strategy to reduce the complexity of large convolution kernels [44; 45]. Therefore, two one-dimensional convolutions can be cascaded to implement the above steps, and deep convolution can be applied to reduce the amount of calculation and parameters. Compared with the fully connected layer, the one-dimensional convolution can take advantage of the parameter sharing and local connection of the convolution layer, further reducing the computational overhead, that is \(O(k_{h}hw+k_{w}hw)\). In addition to adjusting the convolution kernel size as needed, the one-dimensional convolution can also implement other extended convolution operations in the operator, such as introducing the dilated convolution technology [46], which maintains the amount of computation and the number of parameters while increasing the receptive field.