# Road Segmentation and Environment Labeling for Autonomous Vehicles
## 3 System Design
### Clustering--Block B
### Hyperparameter Tuning

Our 3D labeling and segmentation neural network architecture consists of block A--encoders, block B--K-means, block C--decoder, and block D--BPN. Table 2 describes the details of the parameters in the experiment. In the first stage, we randomly sample N = 15K points from the entire point cloud of A2D2 autonomous driving scenes. After filtering the non-ground points, the subsampled point N = 1024 is passed to the training. We train the autoencoder, block A and block C end to end with the Adam optimizer, with a batch size of

Figure 7: BPN performance. (**a**). Two-layer. (**b**). Three-layer. 128 and an initial learning rate of 0.01. Its training length is 400 epochs, but the optimal epoch value is 50 based on low training loss. The value is 0.0022, as shown in Figure 6.

We measured the clustering quality by varying the K value for the subsequent clustering of samples in the latent space with K-means in block B and using the silhouette value to find the similarity between the points in the individual cluster. We found that the silhouette score reached its global maximum at the optimal value of K = 10. We stopped the K-means when the centroids of newly formed clusters did not change. K-means is used only in the training phase for labeling, and the time complexity is less than O (KN). We train the BPN, block D end to end with the gradient descent optimizer with a learning rate set to 0.001. Its training length is 200 epochs, but the optimal epoch value is achieved at 47, since the model achieves a low mean square error (MSE) value of 0.6, as shown in Figure 7.

## 5 Discussion

In the present experiment, we trained autoencoders to cluster objects in the road scene using the data from the front center LiDAR fit in the autonomous vehicle. Moreover, the computer vision might have an ample supply of labeled images. For LiDAR point cloud data, obtaining labels for some fields is highly costly and laborious. Thus, unsupervised clustering of data is indeed a necessity in 3D space. The latent vector's encoded output is sent to the K-means (k = 10) module for clustering. The clustered data are labeled by filtering the non-ground data with a Z value (Z \(\geq-1.0\)), indicating the objects/obstacles above the road surface. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Blocks in Figure 1** & **Parameters** & **Values** \\ \hline A & Number of Samples(N) & 1024 \\ A & Input feature dimension & 5 \\ A & Latent feature dimension (Z) & 3 \\ C & Output feature dimension & 5 \\ A, C & Number of epochs & 50 \\ A, C & Batch size & 128 \\ A, C & Learning rate & 0.01 \\ B & Number of centroids (K) & 10 \\ A & Number of neurons in the & 10 \\ A & hidden layer of encoder & 10 \\ C & Number of neurons in the & 10 \\ A & Activation function & ReLU \\ C & Activation function & Sigmoid \\ A, C & Optimizer & Adam \\ A, C & Loss & Binary crossentropy \\ D & Number of neurons in the & 10 \\  & hidden layer of BPN-two layer & 10 \\ D & Number of neurons in the & 10 \\  & first hidden layer of BPN-three layer & \\ D & Number of neurons in the & 1 \\ D & second hidden layer of BPN-three layer & 200 \\ D & Optimizer & Gradient descent \\ D & Loss & Mean square error (MSE) \\ D & Activation function & Sigmoid \\ D & Learning rate & 0.001 \\ D & Momentum & 0.9 \\ D & Weight decay & 0.0005 \\ D & Learning rate decay & \(1\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A concise list of selected hyperparameters for modules in neural network architecture. ### Unsupervised Feature Learning of Encoder and Decoder

In the unsupervised setting, the number of clusters within the data is not always obvious, especially when some classes may split into multiple clusters or multiple classes sent to the same cluster. Hence, decoding the image after K-means clustering in the latent space is comparatively more beneficial for labeling or identifying the data patterns. The visualization of neighborhoods of different classes on the latent space after clustering is shown in Figure 8a.