Learning to Predict Motion from Raw 3D Object Detections

written by: Christian Neumeyer, Mario Bijelic, Dariu M. Gavrila

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 \DG{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 \DG{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).

You should definitely visit IEEEXplore and take a look on our IV publication:

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