Using Shape, Motion and Context for Object Classification in 3D Point Clouds

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One of the key elements of autonomous driving is the robust and precise detection, tracking and classification of moving objects. In the previous research on this topic, the shape and the movement of an object was used for classification. For this task, object models containing shape and movement information were created based on hand labeled tracks. In this follow-up project, these models should be learned automatically. To separate the objects into different object classes and to improve the prediction of the movement at the same time, the shape, the movement and the context of an object should be taken into account. Humans use the context in a quite natural way. It can be described as a set of implicit rules generated from experience. For example, pedestrians move on sidewalks, sidewalks are beside the roadway and cars move on the roadway. In this research, a method should be developed that learns the context of an object automatically without the use of fixed rules. The expected benefit is the robust classification and prediction of moving objects even in difficult situations. Examples of such difficult situations are incomplete shape information if self-occlusion or occlusion from other objects is present and missing movement information if an observed object is not moving. The additional combination of the context with the shape and the movements is expected to improve robust classification and the prediction of moving objects.