## 4 Validation  
In real traffic scenes, the driver's attention to important traffic elements as well as his immediate cognition and coping with complex road scenes is one of the most important factors affecting driving safety. In the process of driving, drivers can obtain various information of road traffic through visual search. For the driver's visual attention mechanism, the driver will not pay the same attention to all targets in the field of vision during driving, but will pay more attention to the targets or regions of most interest. Thus, it is more efficient to directly detect dangerous targets or regions in front of the road, and pay more attention to some relatively important regional targets in the image. We set the number of searches for targets on each picture to 10, that is, the number of points of attention P to 10, so as to better mine context information, in order to verify that our method can simulate the driver's visual attention mechanism. At the same time, we analyze the sparse traffic scene, the crowded traffic scene, and the intersection traffic scene, respectively, in order to verify that the method can deal with different traffic environments. Figure 9 shows the traffic scenes with sparse vehicles, which is extremely common in country roads. The traffic scenes in graph (**a**) and graph (**b**) are very similar. In these scenes, the vehicles are sparse, but there are targets that may affect the travel in the immediate right front of our vehicles, where graph (**a**) shows cyclist and graph (**b**) shows a red vehicle. In this case, the driver should pay more attention to cyclist and the red vehicle in order to prevent traffic accidents. It can be clearly seen from graph (**c**) and (**d**) that the targets affecting the forward movement of the vehicle are exactly the opposite of those shown in graph (**a**) and (**b**), and are located in the front left of the vehicle. Our method can also accurately locate them. In particular, the car in graph (**d**) is meeting with a car and another car is moving slowly in the distance, in which case the driver will pay full attention to the silver vehicle with which he meets. According to the above analysis, our method can accurately locate the driver's most interesting regions in the traffic scenes with sparse vehicles. Figure 8: The comparison between our method and other networks. (**a**) The comparison of Frames Per Second (FPS); and, (**b**) the comparison of accuracy. Figure 9: \(Cont\). Figure 10 shows the traffic scenes with heavy traffic. For the traffic scenes in graph (**a**) and (**b**), there are many vehicles in the driver's field of vision. Not only are there vehicles parked on both sides of the road, but also vehicles in front of them. At this moment, our car is passing through the middle of the road and it is getting closer to the vehicle in front of the left. The driver needs to pay more attention to the vehicle in front of the left in order to prevent collisions. In the traffic scene in graph (**c**), our car is driving on the highway, and there are many vehicles. Although there are vehicles in front of the own lane, the black vehicle in the adjacent lane is closer to the vehicle and is more prone to collisions. Therefore, the driver will pay more attention to this car. It can be clearly seen from graph (**d**) that the car meets with a white car on a road with many vehicles. In this scene, the driver will pay full attention to the white car meeting with it. From the above analysis, we know that our method can accurately locate the driver's most interesting regions in the traffic scenes with heavy traffic. Figure 11 shows the traffic scenes at the intersection. The road conditions at city intersections are very complex, and drivers need to pay more attention to them. Therefore, the effectiveness of our method can be verified to the maximum extent under this scenario. Graph (**a**) shows the traffic scene, where our car is about to enter the main road from the side road.