## 2 Driver Visual Attention Network
### Information Processing Module (IPM)  
The Information Processing Module (IPM) plays the role of the driver's brain, performs fusion processing on various information, and outputs the final instruction to the Multi-tasking Output Module (MOM). The IPM aims to merge the information \(\rm Y_{i}^{t}\) (i = 1, 2, 3,..., n) output by each C-CNN in the VEM at time t with the information output by the attention point \(\rm P^{t}\) through the full connection  
Figure 3: The schematic diagram of Cross Convolutional Neural Network (C-CNN). Figure 2: The effect diagram of Information Capture (IC). layer. The Gate Recurrent Unit (GRU) processed the merged information, and finally the output \(\rm H_{t}^{t}\) is obtained. For each image block \(\rm I_{i}^{t}\) (i = 1, 2, 3,..., n) extracted by the IC in the t cycle, the C-CNN will perform feature extraction on it to obtain \(\rm Y_{i}^{t}\) (i = 1, 2, 3,..., n). Subsequently, combine multiple features together to obtain \(\rm Y_{S}^{t}\). At the same time, attention point \(\rm P^{t}\) is mapped into the eigenvector \(\rm X_{P}^{t}\) of the same dimension as \(\rm Y_{S}^{t}\) through the full connection layer, and then \(\rm Y_{S}^{t}\) and \(\rm X_{P}^{t}\) are spliced (\(\rm\hat{C}^{\hat{\jmath}}\)) to feature merger, the merged feature \(\rm E^{t}\) can be expressed by Equation (3). \[\rm E^{t}=C^{\hat{\jmath}}\Big{(}Y_{S}^{t},X_{P}^{t}\Big{)} \tag{3}\]  
The Recurrence Attention Model uses RNN to process the time series information. However, the RNN is prone to gradient attenuation and gradient explosion when capturing the large number of time steps in the time series. We adopted GRU [28] instead of RNN in order to solve this problem. GRU is a kind of RNN and a very effective variant of Long-Short Term Memory (LSTM) [29]. When compared with the LSTM network, GRU is simpler in structure and has fewer parameters. It can effectively deal with the problem that RNN is difficult to capture the dependency of time step distance in time series due to gradient attenuation and gradient explosion. Therefore, our method selects three layers of GRU to process the characteristic information \(\rm E^{t}\) with time series, and takes the hidden state \(\rm H_{t}^{t}\) of the last layer of GRU as the final output of the entire GRU mapping. Figure 4 shows the GRU structure. The input of reset gate (\(\rm R^{t}\)) and update gate (\(\rm Z^{t}\)) are both time step input \(\rm E^{t}\) at time t and hidden state \(\rm H_{t-1}\) at the previous time. The output is calculated by full connection layer (\(\rm\sigma\)), with sigmoid as activation function to ensure the output values \(\rm R^{t}\), \(\rm Z^{t}\) \(\rm\in\) [0,1]. Reset gate (\(\rm R^{t}\)) helps to capture short-term dependencies in time series, and \(\rm Z^{t}\) helps to capture long-term dependencies in time series. Formula (4) shows the expression of reset gate (\(\rm R^{t}\)), and the expression of update gate (\(\rm Z^{t}\)) is shown in Formula (5), where \(\rm W_{xx}\), \(\rm W_{xz}\) and \(\rm W_{hr}\), \(\rm W_{hz}\) are weight parameters, and \(\rm b_{r}\), \(\rm b_{z}\) are the deviation parameters. \[\rm R^{t}=\rm E^{t}W_{xx}+H_{t-1}W_{hr}+b_{r}) \tag{4}\]  
\[\rm Z^{t}=\rm r1E^{t}W_{xz}+H_{t-1}W_{hz}+b_{z}) \tag{5}\]  
GRU assists the calculation of hidden state \(\rm H_{t}\) by calculating candidate hidden state \(\rm H_{t}^{h}\). Equation (6) shows the mathematical expression of \(\rm H_{t}^{h}\), where \(\rm tanh\) is the activation function, which can ensure the output value of the \(\rm H_{t}^{h}\)\(\rm\in\) [-1,1]. If the output value of reset gate (\(\rm R^{t}\)) is close to 0, then the \(\rm H_{t}^{h}\) discards the hidden state \(\rm H_{t-1}\) of the previous time step. Additionally, if the output value of reset gate (\(\rm R^{t}\)) is close to 1, \(\rm H_{t}^{h}\) retains the \(\rm H_{t-1}\) of the previous time step, where \(\rm W_{sh}\), \(\rm W_{hh}\) is the weight parameter, \(\rm b_{h}\) is the deviation parameter, and \(\rm\odot\) denotes multiplication by elements.