## 2 Methods
### Monocular Camera to Assist in Determining Lane Changes  
When there are some small obstacles in the lane where the vehicle is located, the vehicle needs to avoid them, as shown in Figure 4. In this case, the method of using the difference of adjacent time heading angles to determine the lane change as described above will be misjudged. Therefore, this paper proposes the use of monocular cameras to assist in determining whether a lane change occurs in a vehicle.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **Vehicle Speed** \\ \(v\) \\ **(m/s)** \\ \end{tabular} & \begin{tabular}{c} **Front Wheel** \\ **Steering Angle (rad)** \\ \end{tabular} & \
\begin{tabular}{c} **Angular Speed of** \\ **Steering (rad/s)** \\ \end{tabular} &
\begin{tabular}{c} **Angular Threshold** \\ **per Unit of Time** \\ **(rad)** \\ \end{tabular} \\ \hline \(v\,\leq\,8.3\) & 0.175 & 0.875 & 0.0175 \\ \(8.3\,<\,v\,\leq\,16.7\) & 0.122 & 0.611 & 0.0122 \\ \(16.7\,<\,v\,\leq\,22.2\) & 0.087 & 0.436 & 0.0087 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Angular threshold for lane change at different speeds of the car.  
Figure 3: Maximum steering speed for different front wheel angles.  
The monocular camera lane-changing algorithm in this paper is mainly based on the intersection value of lane lines on the \(x\)-axis to determine whether the vehicle is changing lane behavior. When the car changes lane to the left, the value of the intersection of the left lane line on the \(x\)-axis will become smaller, while the right lane line will gradually disappear due to the ROI region, as shown in Figure 5a. Therefore, to determine whether a car has changed lanes to the left is mainly by using the value of the intersection of the left lane line on the \(x\)-axis, and whether a right lane change has occurred is judged by using the value of the intersection of the right lane line on the \(x\)-axis. In order to obtain the appropriate threshold value of \(x\)-axis intersection, we carried out a real vehicle experiment in Jiaozhou Bay tunnel in Qingdao. The monocular camera is fixed in the center of the front windshield of the car to shoot the road, and the video is processed using OpenCV to obtain the lane lines and draw the fitted lane lines, as shown in Figure 5b.  
In the test, the speed was 60 km/h. We first made a left lane change, kept the steering wheel still for some distance after the left lane change, and then made a right lane change.  
We find the intersection values of the left and right fitted lane lines on the \(x\)-axis and input them into MATLAB for visualization, as shown in Figure 6.  
Figure 4: Vehicle avoidance obstacle schematic.  
Figure 5: Actual photo of lane in tunnel: (**a**) Vehicle ROI figure; (**b**) Lane Line Fitting Diagram.  
From Figure 6 we can see that the left lane line has two valleys at the \(x\)-axis intersection value, while the right lane line has two peaks at the \(x\)-axis intersection, and these two distinct changes are generated at the beginning of the lane change. Combining the data in the figure, we take the threshold value of 540 for the left lane line at the \(x\)-axis intersection and 100 for the right lane line at the \(x\)-axis intersection. The flowchart of monocular camera-assisted lane change determination is shown in Figure 7.