Using the Transferable Belief Model for Object Classification in LiDAR Data With Geometry, Motion and Context Features

This work presents a method for object classification in LiDAR point clouds based on the Dempster and Shafer theory of belief functions and on its extension, the transferable belief model for the field of vehicle automation. We use a combination of geometric, motion and context features, to model various classes of objects commonly found in a driving scenario according to their expected behaviour in different contexts. Using the models we derive evidence to support a hypotheses about the class of the objects or to identify new types of objects that are not included in the set of modeled classes. We show that the use of contextual information has a positive influence in the results of the classification.