# Road Segmentation and Environment Labeling for Autonomous Vehicles
## 3 System Design
### Clustering--Block B
The decoded output \(x^{\prime}\) given in Equation (8) is shown in Figure 8b. The model achieves a good fit in reconstructing the input, as shown in Figure 8b. As we know from the literature, labeled data for different geographical conditions are extremely rare for road scenes. Multiple output frames can be generated from the latent space without missing essential input features. Although the decoded input looks different from the ground truth image, the AE does learn important non-ground objects, dividers (1), cars (2-4), and trucks (5), as marked in Figure 8b. Figure 8a shows the encoded and clustered output of block C, and Figure 8b is the decoded output of block C, synthesized point cloud data. This synthesized data from block C can be included in the training data for the model's generalizability to detect new scenes. The current proposal is limited to real-time data. ### Pseudo Labeling

Figure 9a,b shows the ground truth segmentation of camera images. Figure 9c,d show the BEV's corresponding LiDAR point cloud data segmentation. The bounding boxes show the vehicles in the road scene matching the color codes given in the segmented camera images in Figure 9b. The pseudo-labeling of the non-ground data using AE and K-means is shown in Figure 9f. The pseudo labels are shown in different colors and converted to relational labels using ground truth segmentation. Figure 8: Output of encoder and decoder. (a). Point cloud clusters in latent space; (b). decoded output of point cloud data. Figure 9: \(Cont.\)

Table 3 shows the six categories of relational labels. The pseudo labels in Figure 9e, f is marked with different labels, as given in Table 3.

The 3D point cloud data segmentation with the class labels' description shows the unified annotations of multiple classes coexisting in the same scene and the deep learning techniques proposed in the present study evolve with increasing training data. ### Generating Automated Labeling and Segmentation Using BPN Network

The learning algorithm for one-hidden-layer feedforward neural networks can learn any set of observations in one short iteration, instead of many learning epochs with acceptable learning and testing accuracy, as shown in Figure 10. The network's learning shows that any continuous bounded activation functions can form disjoint decision regions in multidimensional cases, as discussed in the literature [43]. The output of the two-layer BPN shown in Figure 10a performed better than the three-layer BPN shown in Figure 10b as it was able to label the car, vegetation, divider, and the car next to vegetation. In the case of three-layer BPN, the network mislabeled the car and divider as vegetation. \begin{table}
\begin{tabular}{c c} \hline \hline
**Labels** & **Category** \\ \hline
1 & Car \\
2 & Vegetation \\
3 & The car is adjacent to the divider \\
4 & The car is adjacent to the vegetation \\
5 & Divider \\
6 & Truck \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relational labels. Figure 9: Ground truth segmentation and pseudo labels. (**a**). Ground-truth image; (**b**). ground-truth image; (**c**). ground-truth point cloud data; (**d**). ground-truth point cloud data; (**e**). sample point cloud pseudo labels; (**f**). sample point cloud pseudo labels. ### Evaluation of Segmentation (Supervised Learning)

The evaluation of automated labeling and semantic segmentation here uses the method similar to [54]. In multilabel classification, a misclassification of FPs is better than the no prediction of FNs. The false-positive counts help to optimize algorithms for the misclassified classes. The precision, recall, and F1 score are calculated based on the Equations (14)-(16), respectively, which are listed in Table 4. The model accuracy is calculated based on the Equation (17). The BPN two-layer model performed the best with an accuracy of 75%.