# Road Segmentation and Environment Labeling for Autonomous Vehicles
## 1 Introduction
The latency in a streaming detection system should be improved for object detection, as given in [3]. Therefore, applying the object classification algorithm directly to the point cloud data will achieve a faster judgment in path planning. There are only a few weakly supervised segmentation methods. The approach in [13; 14; 15] tried to capture the geometric structure of the point cloud without converting them into depth maps. The current state-of-art methods for the weakly supervised environment segmentation, classification, and object localization aim to recognize objects individually and learn the data patterns for each group of similar class objects. However, they neglect to visualize the relationship between different class objects. For example, they did not analyze the relational patterns by grouping moving and non-moving objects together, for example, a vehicle adjacent to the divider or a car adjacent to the vegetation. As far as our knowledge, very few group-wise weak supervised algorithms are available in the industry for image and point cloud data segmentation. [PERSON] et al. [16] proposed group-wise learning to understand the relationship between two images by capturing the semantic context and underlying visual patterns. In this work, a graph neural network conducts multiple iterations to discover semantic information and meaningful relationships among a group of images but does not learn about the relationship between different object classes within the same image. In [17], the authors proposed a 3D object-object relationship graph model using geometric appearance and spatial features of point cloud data. The authors learned about the relationship between different objects but the extracted patterns are specific to each object class for the 3D bounding box proposal using supervised learning. The onboard perception system would require a database with different group-wise label categories based on relational attributes, such as moving and non-moving objects to deploy autonomous vehicles safely. Keeping in mind the limitations of the previous unsupervised learning approaches for group-wise or relational learning for 3D structures,we have conducted a pioneer attempt to introduce relational labels for road scene segmentation. Autoencoders combined with K-means are a less expensive way to cluster the unlabeled data. They lead to performance gains across various architectures and labeling strategies. Furthermore, the relational labeling technique using unsupervised learning is combined with supervised learning methods in order to improve the accuracy and refine the model predictions for the segmentation task. The main contributions of the present research study are as follows:  
1. In the study, an object labeler operates directly from bird's eye view (BEV) LiDAR point cloud data and reduces the computational burden by processing only the non-ground data. 2. The human-labeled dataset is not sufficient for autonomous vehicles. It is crucial to enhance the label in order to improve detection and segmentation accuracy in real-time. The research introduces unsupervised techniques to create pseudo-labels combined with a supervised technique to build an automated labeling system to solve the problem. 3. Our method can quickly encode point clouds by learning the relationship between two objects, obtaining the spatial feature of point clouds, labeling multiple categories, and predicting various categories. These labels can help train the network and perform 3D segmentation in real-time.