The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 24, 2025

Filed:

Apr. 20, 2022
Applicant:

Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US);

Inventors:

Alan Sullivan, Middleton, MA (US);

Siheng Chen, Cambridge, MA (US);

Jun Wang, Greenbelt, MD (US);

Xiaolong Li, Blacksburg, VA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/20 (2017.01); B60W 60/00 (2020.01); G06V 10/80 (2022.01); H04N 19/43 (2014.01);
U.S. Cl.
CPC ...
G06T 7/20 (2013.01); B60W 60/0027 (2020.02); G06V 10/806 (2022.01); H04N 19/43 (2014.11); G06T 2207/10028 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30252 (2013.01);
Abstract

The present disclosure provides a system and a method for motion prediction for autonomous driving. The system disclosed herein provides an efficient deep-neural-network-based system to jointly perform perception and motion prediction from 3D point clouds. This system is able to take a pair of LiDAR sweeps as input and outputs for each point in the second sweep, both a classification of the point into one of a set of semantic classes, and a motion vector indicating the motion of the point within the world coordinate system. The system includes a spatiotemporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion. The training of this system is regularized with spatial and temporal consistency losses. Thus providing an improved motion planner for autonomous driving applications.


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