Systems and methods for predicting the trajectory of a moving object

US12168461B2 · kind B2 · utility

Assignees

• TOYOTA RESEARCH INSTITUTE, INC. · US
• Massachusetts Institute of Technology · US

Inventors

• Xin Huang · Xi'an, CN
• Igor Gilitschenski · Cambridge, US
• Guy Rosman · Newton, US
• Stephen G. McGill · Broomall, US
• John Joseph Leonard · Newton, US
• Ashkan Mohammadzadeh Jasour · Cambridge, US
• Brian Williams · Melbourne, AU

Key dates

• Filing date: Dec 1, 2021
• Grant date: Dec 17, 2024
• Expiry date: Mar 9, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N7/01
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

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.