# Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition
## 4 Experimental Results
### Comparison  
To test the behavior of the trained models, dataset DR2 is used. A further evaluation is performed with the dataset DR3, which has been obtained with a modified circuit and placed in a different context. The first model is an end-to-end model, which combines a CNN to process the input images and an MLP, following proposals such as that in Reference [14]. The second model uses a first encoder combined with an MLP. The encoder is trained with the entire Autoencoder to learn the identity function, i.e., the image from the camera is used at the same time at the input and output of the Autoencoder. This approach is the same as in Reference [18]. None of these models rely on semantic segmentation, but the experiments can be performed with both the augmented datasets and without augmentation.  
Figure 15 shows the corresponding correlation graphs for dataset DR3.  
The values of the correlation coefficients degrade slightly for the proposed model when the DR3 dataset is used. For the end-to-end model, the degradation is relatively high. This can be explained by the fact that this model can base its decisions on large amounts of information from the images, so it does not have to focus exclusively on the road lines to learn the driving task. It can make decisions based on additional and spurious elements that, in the case of the proposed model, are eliminated with semantic segmentation. Since the DR3 dataset is based on a different circuit, placed differently and with different lighting, the end-to-end model will have more trouble determining the steering angle.  
Figures 16 and 17 show the driving sequences for dataset DR3, comparing the performances of the proposed model and the other models (all working at 160 \(\times\) 120 pixels).  
The proposed model is relatively accurate when imitating the behavior of the human driver. The other two models that do not rely on semantic segmentation show a greater deviation from human behavior. It also makes some unexpected turning decisions. In the case of the end-to-end model, numerous spikes in the graphs indicate more erratic behavior when interpreting the images and making decisions.  
The obtained correlation coefficients are summarized in Table 3.  
Various issues can be identified. The correlation is lower when using the alternative dataset DR3. There is also a reduction when using images with a smaller size, making this a relevant aspect: getting a better performance implies the need for greater computing capacity. Using the Canny Edge Detector approach provides similar results to those of the proposed model, while the use of input noise for training the Autoencoder seems to be essential for a good performance.  
Both the end-to-end model and the Autoencoder without semantic segmentation show lower correlation coefficients. They benefit greatly from the proposed dataset augmentation using image overlay. This implies that the proposed model is more efficient in this task and that image superposition achieves better results with reduced datasets.  
Figure 15: Correlation graphs for the proposed (**a**,**b**), the end-to-end (**c**,**d**) models, and the Autoencoder without semantic segmentation (**e**,**f**). Expected vs. predicted steering values with the dataset DR3 and only test. Training is performed with the augmented dataset.  
Figure 16: Comparison of the steering values provided by a human driver and the trained models, 160 \(\times\) 120—driving direction right. (**a**) Proposed model. (**b**) End-to-end model. (**c**) Model w/o segmentation.  
Figure 17: Comparison of the steering values provided by a human driver and the trained models, 160 \(\times\) 120—driving direction left (**a**) Proposed model. (**b**) End-to-end model. (**c**) Model w/o segmentation.