## 3 Experiment
### The Setting of Loss Function  
The loss function \(L_{S}\) of our network includes three parts, which are the loss part for classification \(L_{C}\), the loss part for attention points \(L_{P}\), and the loss part for location \(L_{L}\). Additionally, we can optimize the entire network structure by minimizing \(L_{S}\). The loss of classification part (\(L_{C}\)) : Our method employs the cross entropy loss function to measure the accuracy of the predicted distribution \(C^{t}\) and the real distribution \(C^{g}\) of each category. Additionally, Formula (12) shows the expression of the loss part for classification \(L_{C}\), where N represents the number of categories and \(C^{g}_{k}\) and \(C^{t}_{k}\) represent the \(k\)-th component of \(C^{g}\) and \(C^{t}\), respectively. \[L_{C}=\sum_{k=1}^{N}-C^{g}_{k}\ln\left(C^{t}_{k}\right) \tag{12}\]  
The loss of attention point part (\(L_{P}\)): The role of attention points is to provide C-CNN with feature points for extracting information. The prediction process by constraining attention points with \(L_{P}\) can ensure more accurate local feature extraction. We use the strategy reward mechanism in reinforcement learning [33] to optimize the decision process of attention points, according to the particularity of attention point prediction. Figure 6 shows the optimization process. In our work, the Agent represents the attention point \(P^{t+1}\) to be predicted. The action of the attention point \(P^{t}\) represents the search of the driver's interest target or region by the attention point. The Reward is the overlap between the predicted frame and the real frame. The Environment represents the currently processed image. The state represents the accuracy of the search for the driver's region of interest by the attention point. Additionally, \(R^{t+1}\) and \(S^{t+1}\) are the inputs of Environment to Rward and state, respectively. Moreover, we adopted the L2 loss function to constrain the attention point in the last step, forcing it to approach the center of the target, in order to more accurately simulate the driver's visual attention mechanism. Accordingly, the loss part \(L_{P}\) of the attention process is expressed as follows, where \(\pi(r,^{\prime\prime},r)\) is the distribution of attention points \(P^{t}\), \(b^{t}\) is the expectation of \(R^{t}\), and \(R^{t}\) can be optimized by scoring under the L2 criterion \(O^{t}_{s}\) to estimate. \[L_{P}=-\sum_{t=1}^{T}\ln(\pi\left(P^{t}\left|\left(u^{t}_{P},\sigma^{2}\right) \right)\left(R^{t}-b^{t}\right)+\frac{1}{T}\sum_{t=1}^{T}\left(O^{t}_{s}-R^{t} \right)^{2}+\parallel u^{t}-\left(O^{t}_{yx}+\frac{1}{2}O^{t}_{hw}\right) \parallel_{2}^{2} \tag{13}\]  
The loss of locating part (\(L_{L}\)): Drivers will pay more attention to dangerous targets or regions ahead of the road during driving. Therefore, we only detect the targets that are most interesting to the driver in the picture in order to simulate this process in a real scenario. The coordinates of the upper left point are \(O^{t}_{yx}\) and the size is \(O^{t}_{hw}\). We estimate the predicted amount \(O^{P}_{yx}\) of the location by means  
Figure 6: The training process of attention point. of the maximum contingent estimation. In addition, we also construct a loss function that is related to the predicted border size \(\mathrm{O}_{\mathrm{hw}}^{\mathrm{P}}\) by predicting the overlap (IOU) of the border and the real border. At the same time, the accuracy of the predicted border is improved by maximizing the IOU. The loss of the positioning portion can be expressed as Equation (14).