Massachusetts Institute of Technology

Toyota Research Institute, Inc.

Ashkan Mohammadzadeh Jasour

Guy Rosman

John J Leonard

Igor Gilitschenski

Xin Huang

Stephen G Mcgill

Brian C Williams

Systems and methods for predicting a trajectory of a moving object are disclosed herein. One embodiment downloads, to a robot, a probabilistic hybrid discrete-continuous automaton (PHA) model learned as a deep neural network; uses the deep neural network to infer a sequence of high-level discrete modes and a set of associated low-level samples, wherein the high-level discrete modes correspond to candidate maneuvers for the moving object and the low-level samples are candidate trajectories; uses the sequence of high-level discrete modes and the set of associated low-level samples, via a learned proposal distribution in the deep neural network, to adaptively sample the sequence of high-level discrete modes to produce a reduced set of low-level samples; applies a sample selection technique to the reduced set of low-level samples to select a predicted trajectory for the moving object; and controls operation of the robot based, at least in part, on the predicted trajectory.

Systems and methods for predicting the trajectory of a moving object

Growing community of inventors

Cambridge, MA, United States of America

Ashkan Mohammadzadeh Jasour