NVIDIA Corporation
FUTURE OBJECT TRAJECTORY PREDICTIONS FOR AUTONOMOUS MACHINE APPLICATIONS

Abstract:

In various examples, historical trajectory information of objects in an environment may be tracked by an ego-vehicle and encoded into a state feature. The encoded state features for each of the objects observed by the ego-vehicle may be used--e.g., by a bi-directional long short-term memory (LSTM) network--to encode a spatial feature. The encoded spatial feature and the encoded state feature for an object may be used to predict lateral and/or longitudinal maneuvers for the object, and the combination of this information may be used to determine future locations of the object. The future locations may be used by the ego-vehicle to determine a path through the environment, or may be used by a simulation system to control virtual objects--according to trajectories determined from the future locations--through a simulation environment.