# Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition
## 4 Experimental Results
### Additional Validation Experiments  
Additional validation is carried out by testing the automated driving capacity in situations that present certain complications--in particular, those containing highlights, shadows, occlusions, and illumination changes. Based on dataset DR3 with images sized at 160 \(\times\) 120, variations are generated in which image sequences are randomly modified:  
1. [label=()]
2. segments of the images are blackened or whitened, simulating either occlusions or intense reflections. 3. darkening or lightening of segments or the entire image, simulating either shadows or light reflections or general changes in lighting. The modifications (segment size and intensity) are generated randomly, with some limitations to avoid excessive image deterioration. This approach allows an evaluation of the results based on the semantic segmentation provided by the dataset. Table 4 shows the correlation coefficients for the best models from the previous experiments. The same sequence of the modified images is used for each model. The coefficients show certain patterns of behavior. The worst results are obtained when a section of the image turns white. This makes sense, since the key elements to detect are the white lines. In contrast, the occlusion with a black region does not pose many problems for the proposed model. Both the model based on the Canny edge detector and the model trained without input noise suffer considerably in the results. Correlation values near 0.8 represent models with either a poor approximation capability or extreme outliers. The fact that the difference is so remarkable between the proposed model trained with noise and without noise leads us to suppose that this aspect may be fundamental to obtaining a more robust model. A physical simulation was also carried out to determine the behavior of the Autoencoder in the indoor environment. A sample is shown in Figure 18. In most cases, the reconstruction is correct except for the affected area, and although occasionally the Autoencoder only delivers a partial reconstruction, it is usually adequate to complete the driving task. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **w/o Change** & **White** & **Black** & **Light** & **Dark** \\ \hline Proposed model & 0.9474 & 0.9026 & 0.9404 & 0.9284 & 0.9431 \\ Proposal w/o input noise & 0.9079 & 0.7859 & 0.8325 & 0.8203 & 0.8402 \\ Canny Detector + CNN & 0.9462 & 0.8177 & 0.8675 & 0.8471 & 0.8684 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Correlation coefficients for dataset DR3 at 160 \(\times\) 120 with image modifications. Considered is the total whitening or blackening of an image section or slight lightening or darkening of a partial section or the full image. The first column contains the correlation coefficients for the original dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **DR2** \\ **320 \(\times\) 240** \\ \end{tabular} & \begin{tabular}{c} **DR2** \\ **160 \(\times\) 120** \\ \end{tabular} & \begin{tabular}{c} **DR3** \\ **320 \(\times\) 240** \\ \end{tabular} & \
\begin{tabular}{c} **DR3** \\ **160 \(\times\) 120** \\ \end{tabular} &
\begin{tabular}{c} **Dataset** \\ **Augmentation** \\ \end{tabular} \\ \hline Proposed model & 0.9762 & 0.9629 & 0.9514 & 0.9474 & Yes \\ Proposal w/o input noise & 0.9368 & 0.9272 & 0.9354 & 0.9079 & Yes \\ Canny Detector + CNN & 0.9798 & 0.9607 & 0.9533 & 0.9462 & Yes \\ CNN w/o segmentation & 0.9623 & 0.9542 & 0.9295 & 0.9156 & Yes \\ AE w/o segmentation & 0.9424 & 0.9396 & 0.9151 & 0.9015 & Yes \\ CNN w/o segmentation & 0.9382 & 0.9378 & 0.8902 & 0.8823 & No \\ AE w/o segmentation & 0.9347 & 0.9219 & 0.8931 & 0.8745 & No \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlation values for all the models, datasets, and image sizes. More details are shown in the repository for this project [39]. Video 6 shows the behavior of the car when encountering different types of difficulties. A final trial is performed with a different-shaped track, shown in video 7.