## 3 Experiment
### Experimental Results and Analysis  
We constructed the Driving Behavior Information Network (DBIN), which is used to establish the one-to-one correspondence between driver horizon information and driving behavior. The change of loss function during training is shown in Figure 6a. The accuracies are measured within \(6^{\circ}\) or 5 km/h biases. In addition, the results of the accuracy of the test set and classical convolutional networks, such as DensNet169 [25], incidence v3 [26] and VGG16 [27] are compared. The results are shown in Figure 6b.  
Figure 5: The part of data set display diagram.  
It can be seen from Figure 6a that as the training progresses, the value of the loss function continues to decrease and eventually stabilizes when iterating over 100 epochs. Moreover, we can clearly see from Figure 6b that DBIN has higher accuracy than several other models, and obtains good results.  
In Figure 7, the red area represents the driver's main attention area and it can be seen that the current traffic scene depicts our car following the white vehicle through the zebra crossing. At this moment, the driver will pay more attention to the distance from the vehicle and observe whether there is a pedestrian on the zebra crossing. After analysis, we can see that the driver's main attention area obtained by our method accords with the driver's selective attention mechanism. Because the accuracy of DBIN is higher, the display effect in Figure 7d is the best, and the determined driver's main attention area is also the most accurate.  
Figure 8 shows the comparison graph of DBIN experimental results at different training stages. Among them, Figure 8a-c represent the early, middle and last three stages of training, respectively. It can be clearly seen from the figure that the driver's main attention area is incomplete and inaccurate in the early stage of training. Some images show that the attention area is only a tiny part of the front windshield, and some show the full screen as the attention area, which is obviously abnormal. However, with the training, the driver's attention area gradually changes and eventually becomes accurate and complete.  
Figure 6: The Experimental results. (**a**) Description of the change of loss function during training; (**b**) Description of the comparison of accuracy.  
Figure 7: The comparison graph of different network test results. (**a–d**), respectively, represent the experimental results of DensNet169, VGG16, InceptionV3 and Driver Behavior Information Network (DBIN).  
In Figure 8, there are three traffic scenarios from top to bottom. The first and second traffic scenarios are similar in that our car passes on the road where the vehicle stops on the right side, but the difference is that our car in the first scenario is closer to the parked vehicle on the right side. For the first traffic scene, the driver's speed is 4 km/h, and the steering wheel turns 30\({}^{\circ}\) to the left. Obviously, the driver is slowly moving the vehicle to the left to prevent the collision with the vehicle on the right. Therefore, the driver's main attention area will be in the right front of the vehicle. While in the second traffic scene, our car is far away from the vehicle on the right and the front view is wide. At a speed of 20 km/h, the driver turns the steering wheel 5\({}^{\circ}\) to the left, and it is obvious that the driver is crossing the street at a low speed. Therefore, the driver puts the main attention area in front of the car. The third traffic scene is that our car passes through the bridge, which is dangerous to some extent. In this scene, there are no vehicles around, and the driver has a wide field of vision. At the moment, the speed of the car is 57 km/h, and the steering wheel turns 7\({}^{\circ}\) to the left. It can be seen that the driver is crossing the bridge at normal speed. Therefore, the driver will only focus on the lane ahead and the distance from the vehicle ahead.