Road Segmentation and Environment Labeling for Autonomous Vehicles

Abstract

In autonomous vehicles (AVs), LiDAR point cloud data are an important source to identify various obstacles present in the environment. The labeling techniques that are currently available are based on pixel-wise segmentation and bounding boxes to detect each object on the road. However, the Avs’ decision on motion control and trajectory path planning depends on the interaction among the objects on the road. The ability of the Avs to understand the moving and non-moving objects is the key to scene understanding. This paper presents a novel labeling method to combine moving and non-moving objects. This labeling technique is named relational labeling. Autoencoders are used to reduce the dimensionality of the data. A K-means model provides pseudo labels by clustering the data in the latent space. Each pseudo label is then converted into unary and binary relational labels. These relational labels are used in the supervised learning methods for labeling and segmenting the LiDAR point cloud data. A backpropagation network (BPN), along with traditional gradient descent-based learning methods, are used for labeling the data. Our study evaluated the labeling accuracy of two as well as three layers of BPN. The accuracy of the two-layer BPN model was found to be better than the three-layer BPN model. According to the experiments, our model showed competitive accuracy of 75% compared to the weakly supervised techniques in a similar area of study, i.e., the accuracy for S3DIS (Area 5) is 48.0%.

Keywords

• point cloud preprocessing
• point cloud labeling
• semi-supervised learning
• autonomous vehicle