# Road Segmentation and Environment Labeling for Autonomous Vehicles
## 3 System Design
### Clustering--Block B
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Network** & **P** & **R** & **F1** & **Accuracy** \\ \hline BPN two-layer & 75\% & 75\% & 75\% & 75\% \\ BPN three-layer & 44.4\% & 57.1\% & 49.95\% & 50.75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Micro averaging for point cloud data segmentation. Figure 10: Labeling and segmentation output on the test data using BPN (supervised learning). (**a**). Labeling and segmentation with two-layer BPN. (**b**). Labeling and segmentation with three-layer BPN. To gain deeper insight into our model, we provided several typical cases where our approach fails on the A2D2 dataset. In Figure 10, we illustrate the labeling for different semantic categories. As observed in the output, the failures are mainly due to (i) incorrect recognition as a result of some similar pattern between the object classes (e.g., vegetation and divider) or (ii) misclassified small or occluded objects (e.g., truck or car). We compared our result with other state-of-art methods in a similar field of study. In group-wise learning [16], the segmentation accuracy of images is 68.7%; for point cloud, weakly supervised segmentation with fewer labels [10], the segmentation accuracy of a real-time point cloud dataset S3 DIS (Area 5) is 48.0%. In the same study area as ours, the accuracy of different datasets shows that more labeling techniques are required to improve the segmentation task for group-wise learning with unsupervised learning methods. ## 6 Conclusions

In the present research, 3D point cloud data labeling is considered semi-supervised learning, as it is streamlined using both unsupervised and supervised learning models. The geometric properties of data lie in the lower dimension rather than the actual input space, and each data point has a set of neighbors connected by Euclidean distance. In the proposed architecture, the unsupervised learning model labels the poorly labeled data into the relational label. Dimension reduction is much better than the down-sampling techniques, since it preserves spatial information without losing the data patterns. The experiment reduces the original point cloud data dimensionality to 30% using autoencoders. The decoder generates new images, and K-means creates the subset named pseudo labels of the n-dimensional Euclidean space. The synthesized image can help improve the generalizability property of the model for segmenting new scenes. Each pseudo label is compared with the ground truth and converted into relational labels. These relational labels are also called class labels. The backpropagation feedforward neural networks are trained using these class labels to label the unlabeled data. The labels are evaluated by finding the true positive, false positive, and false negative. To improve accuracy, the supervised model uses multiple learning iterations with more labeled data and optimization techniques. The two-layer BPN model performed better than the three-layer BPN model. The synthesized data can also be included in training the BPN models for future investigations. The study offers an opportunity to improve the methodology or application further to merge multiple frame receivers from other sensors by observing similarities and dissimilarities, and labeling the relationship between different frames. While the present work focuses on clustering and labeling models from BEV, it is possible to extend the models to incorporate data across multiple views from LiDAR. By including point cloud augmentation algorithms, rotation, and homogenous transformation matrixes, we can change the viewpoint to a global coordinate system in the model hyper parameters, and integrate AE with rotational and angle invariant transformer networks, such as spatial transformer and T-net models. The labeled dataset can be used in supervised learning for scene segmentation. One of the downsides of the methodology is that we have to create our initial clusters based on the data encoded by the untrained network.