# Research on Vehicle Trajectory Prediction and Warning Based on Mixed Neural Networks
## 4 Results and Discussion
Surprisingly, although the accuracy was quite low, both x and y model could predict accurately when we verified the model. In addition, the processing time was only 12 milliseconds, and the losses were 0.000248 for the x model and 0.000932 for the y model. The training results of the x and y coordinates of the car trajectory model are demonstrated in Table 11, which contains the processing time and score value of evaluation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time in Video**} & \multicolumn{2}{c}{**FPS**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{2-5}  & **Max** & **Min** & **Max** & **Min** \\ \hline
1-5 s & 10.995 & 10.868 & 0.88 & 0.72 \\
5-10 s & 10.202 & 9.461 & 0.91 & 0.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The evaluation results of videos of the car detection model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Training**} & \multirow{2}{*}{**Training**} & \multicolumn{2}{c}{**Loss**} & \multicolumn{2}{c}{**Average Loss**} \\ \cline{2-5}  & **Time** & **Max** & **Min** & **Max** & **Min** \\ \hline
1 & 5 min 58 s & 106.00877 & 0.96654 & 106.00877 & 3.2454 \\ \hline
2 & 5 min 57 s & 1.05844 & 0.69618 & 0.98139 & 0.83406 \\ \hline
3 & 15 min 10 s & 1.06956 & 0.22245 & 0.80912 & 0.39897 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The training results of the car detection model. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Test Image**} & \multicolumn{2}{c}{**Total**} \\
**Number** & **Processing Time (s)** & **Accuracy** \\ \hline
1–10 & 0.3637 & 0.75 \\
11–40 & 0.9518 & 0.86 \\
50 & 0.132 & 0.71 \\
65 & 0.159 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The evaluation results of images of the car detection model. Figure 21: _Cont._  
The car trajectory prediction results are shown in Figure 23. The ground truth road images were used to be our input. Then, we used car object detection first, and applied car trajectory prediction. We found that the car trajectory model had the ability to predict the future trajectory quite well, as seen in in Figure 18. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{
\begin{tabular}{c} **Training** \\ **Time** \\ **(s)** \\ \end{tabular} } & \multicolumn{2}{c}{**Accuracy**} & \multirow{2}{*}{**Loss**} & \multicolumn{2}{c}{**Evaluation**} & \multicolumn{2}{c}{**Evaluation**} \\  & & & & & **Time** & & **Score** \\ \cline{3-8} \cline{8-8}  & **Max** & **Min** & **Max** & **Min** & **(ms)** & **Acc** & **Loss** \\ \hline Predict & & & & & & & & \\ X & 6.829 & 0.33396 & 0.00104 & 0.07694 & 0.00017 & 12 & 0.22222 & 0.000248 \\ coordinates & & & & & & & & \\ \hline Predict & & & & & & & & \\ Y & 6.75986 & 0.33357 & 0.00054 & 0.02107 & 0.00098 & 12 & 0.11111 & 0.000932 \\ coordinates & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The training results of car trajectory prediction model. Figure 21: Lane line detection. We demonstrated the entire process of lane line detection in five rows: **(a)** the ground truth, **(b)** find four points, **(c)** edge detection, **(d)** projection transformation, **(e)** and **(f)** find curve equation, **(g)** draw lane area, **(h)** projection transformation again; **(i)** and **(j)** are the results of the final lane line detection. Figure 22: Cont. Figure 23: _Cont._  
Figure 22: The training of the car trajectory prediction model. We showed the results in four rows: (**a**) the accuracy of predicting x coordinates model, (**b**) the loss of predicting x coordinates model, (**c**) the accuracy of predicting y coordinates model, (**d**) the loss of predicting y coordinates model; (**e,f**) compare the real x coordinates and predicted x coordinates. The blue line is x coordinates predicted by our model, and the orange line is the real x coordinates; (**g,h**) compare the real y coordinates and predicted y coordinates. The blue line is y coordinates predicted by our model, and the orange line is the real y coordinates. Figure 23: Cont.