Learning to Predict Motion from Raw 3D Object Detections

Abstract

We show how to design a motion prediction algorithm that works with 3D object detections and map locations. In particular, we obtain object id’s – even though the training data does not contain any object id’s – across multiple time-steps into the future by propagating a Gaussian Mixture of likely object (e.g., vehicle) locations through time.We validate our approach on the nuScenes dataset. First, we find that a motion prediction algorithm without tracking id’s performs as well as motion prediction algorithm with tracking id’s in the training data. Second, the 3D labels of an on-board perception system are inferior (e.g., loss of detections, positional uncertainty) to those generated by offline labelling (automatic labelling pipeline, manual labelling). Even so, we find that a moderate increase in the size of the training data offsets the deterioration in prediction performance (with no additional offline labelling).