## 3 Results
### Simulation of Monocular Camera Lane Change Judgment  
We put the video taken from the real car into OpenCV for simulation, the results of the simulation are shown in Figures 14 and 15. From Figures 14 and 15, the monocular camera can be a good aid to determining whether the vehicle is changing lanes, but the lane change judgment of the monocular camera will have a lag, and the moment of lane change is judged by the lane line intersection  
Figure 14: Images of the start and end moments of the vehicle lane change to the left: (**a**) Image of the start moment of the vehicle lane change to the left; (**b**) Image of the end moment of the vehicle lane change to the left. Figure 13: Error of the lateral positioning algorithm. Figure 15: Images of the start and end moments of the vehicle lane change to the right: (**a**) Image of the start moment of the vehicle lane change to the right; (**b**) Image of the end moment of the vehicle lane change to the right. is often later than the real start moment of the lane change. In order to find out the lag time of the monocular camera lane change judgment, we simulated a section of car motion trajectory in the simulation software Prescan to obtain the exact lane change time point of the vehicle. Then the vehicle driving video is processed in OpenCV to obtain the start moment of the lane change judged by the monocular camera, and the lag time can be found by comparing the two moments. Create a road in Prescan and give the vehicle a driving trajectory as shown in Figures 16 and 17, where the width of each lane is 4.5 m and the initial speed of the vehicle is 18 m/s and is kept at a constant speed. The difference in heading angle for each lane change of the car is \(8^{\circ}\). The vehicle made two lane changes while driving, one to the left at 10 s and one to the right at 21.8 s. The simulation in OpenCV determines the start of a lane change by the number of frames in the video, with a frame rate of 20 frames per second. The moment when the vehicle starts to change lanes, as determined by the monocular camera simulated in OpenCV, is shown in Figure 18.  
Through the above data we can obtain the difference between the two times, the specific results are shown in Table 2.  
Figure 16: Vehicle track information. Figure 17: Vehicle track images. Figure 18: The start moment of the vehicle changing lane to the left and to the right as judged by the monocular camera: (**a**) The moment when the vehicle starts to change lanes to the left; (**b**) The moment when the vehicle starts to change lanes to the right. From Table 2, we can see that the difference between the moment judged by the monocular camera and the real moment during the second lane change is even larger at 1.7 s. Thus, we set the lag time to 2 s. When the difference in the heading angle of the vehicle within the adjacent time is greater than the threshold value, the lateral displacement of the vehicle is calculated based on the lateral positioning algorithm at this time first, and the lateral displacement calculation of the vehicle takes effect when the vehicle receives the lane change signal from the monocular camera within 2 s; otherwise, the vehicle is deemed not to have changed lanes. The simulation and experiment of the monocular camera mentioned above are aimed at the case where the lane width is 4.5 m. We found that the lane widths in different tunnels in China have the following different widths: 3 m, 3.5 m, 4 m, and 4.5 m, respectively. In order to make the system more applicable, we take the threshold value of the intersection point for the lanes with different widths. The results of the thresholds are shown in the Table 3.  
We conducted a simulation test of lane change judgment with a monocular camera for lanes of different widths. The specific test results are shown in the figure below.