# Research on Vehicle Trajectory Prediction and Warning Based on Mixed Neural Networks
## 4 Results and Discussion
At last, we showed the results of the combination of all techniques. Figure 24 shows the first results of the validation. All the images in Figure 24 show the three techniques, namely car object detection, lane line detection, and car trajectory prediction. The images (a), (b), and (f) in Figure 24 show excellent results. However, as seen in images (c), (d), and (e) in Figure 24, while the car detection and car trajectory prediction performed quite well, unfortunately, we found that the lane area in these three pictures was slightly wrong due to other vehicles blocking our lane line. In image (d), the car on our left side crossed the lane so as to block the lane line. It caused the process of lane line detection to obtain the wrong curve equation. That is to say, the detection of the lane area was wrong. We considered that to be reasonable. If other vehicles were to cross the lane, we definitely would not be able to see the lane line as well due to the lane line being blocked by others. Figure 25 shows the three techniques used, including car object detection, lane line detection, and car trajectory prediction. We could assume that there were two crucial factors that would affect the lane detection based on the results shown in Figures 24 and 25. First of all, when one of our lane lines would be blocked by other cars, the detection of the lane area would be wrong. Sec-ondly, if the car in front of us would be very close to us, the lane area detection would likewise be wrong. The proposed system must achieve the promised results of driver safety, which include object detection of the car, lane line detection, and trajectory prediction of the car, and the system needs to detect at each frame. The object detection and trajectory prediction of the car must not be affected by other cars crossing the lane line and they must detect efficiently. In lane line detection, two lane lines are required to find curve equations. Thus, the equations cannot be found if a vehicle crosses the line at a certain frame. Moreover, we are not capable of seeing the line blocked by the vehicle as well when we are sitting in the driver's seat. If the two lines are not blocked by vehicles, we can obtain good lane line detection results. It is when a vehicle is crossing the lane line and is very close to us that  
Figure 23: The car trajectory prediction results. We demonstrated the results in four rows: (**a,c**) real trajectory, (**b,d**) compare real trajectory and the trajectory predicted by our model. The green line is the real trajectory of the car, while the red line is the future trajectory predicted by our model. our lane area will be affected. Although the vehicle crossing the line may influence the lane line detection, such as deformation of the lane area, it can still warn the drivers to slow down or take other measures. In addition, object detection and trajectory prediction of the car can help to remind the drivers as well. Therefore, based on the combination of multiple models to warn the drivers and enhance driving safety, our results are still promising. Figure 24: The combination of all techniques of the first validation. We demonstrated the results in three rows: **(a)** the first second the vehicle enters the sight from our left side, **(b–e)** the vehicle moves forward from our left side, **(f)** the last second the vehicle moves forward from our left side.