## 7 Discussion
### Comparison Between Systems  
After assessing the tests carried out using each approach and analyzing the figures and tables above, several conclusions can be reached.  
It is easy to conclude that using the neural network to drive the car shows much better performance. When the neural network is used, the robot manages to achieve a much higher number of consecutive laps compared to the classical solution (Table 3).  
The neural network uses all the information on the image, and all pixels are used to obtain a solution, instead of the classic approach, which processes a part of the image to search for relevant and useful data. In frames where the classical vision algorithm fails to detect lines or where there are no lines to be detected, it only uses the acquired information, meaning that it lacks information, while the neural network takes advantage of the entire image. Although the network was trained using images created from classical programming, the neural network approach has the ability to calculate/predict situations based on similar images, meaning that it is able to cover a greater space of possible results.  
Figure 14: Track top view and car’s position with a maximum velocity of 15 rad/s for: (a) 1 lap, (b) 10 laps, and (c) 100 laps.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Velocity** & **CV** & **NNM** \\  & **(rad/s)** & **Algorithm** & **NNM** \\ \hline  & 10 & 23.5 & 100 \\  & 12.5 & 13.5 & 100 \\
**Average** & 15 & 7 & 65 \\
**number of laps** & 17.5 & 4.5 & 41 \\  & 20 & - & 5.33 \\ \hline  & 10 & 82 & 100 \\  & 12.5 & 43 & 100 \\
**Maximum** & 15 & 20 & 100 \\
**number of laps** & 17.5 & 10 & 71 \\  & 20 & - & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison table: showing average and maximum number of laps.  
The solution that uses classic computer vision is more complex in terms of programming, and is not able to cover all possible cases encountered on the road. With the neural network approach, the car performs much more fluidly and coherently in special cases such as intersections and zebra crossing, resulting in fewer deviations.  
In Table 4, is noticeable that the time required for the car to drive around the track in the simulator is longer in the solution with neural networks. This happens not because it takes longer to process the image, as the time frame was previously defined as 100 ms for both cases, but because the simulator takes longer to display it graphically. This is due to the fact that the processor is much more busy in computational terms due to the amount of resources that the neural network requires. On average, the CV solution takes 98 ms to complete a single frame and the NNM solution takes 90 ms.  
The computer vision approach takes 121.3% of the processor and the CoppeliaSim software takes 91.4%. The NNM approach takes 194.4% of the processor, while the CoppeliaSim takes 88.7%. As the times per lap were read in real time, and because CoppeliaSim is slower when using the NNM approach, the simulated lap times were longer than the real ones.  
It is crucial to highlight the significant success achieved in our tests using the NNM, very much surpassing the solution developed with the computer vision algorithm. These excellent results motivated us to test the developed neural network in a real robotic car.