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:
Feb. 20, 2024

Filed:

Jan. 08, 2019
Applicant:

The Regents of the University of California, Oakland, CA (US);

Inventors:

Akshay Rangesh, San Diego, CA (US);

Mohan M. Trivedi, San Diego, CA (US);

Nachiket Deo, San Diego, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01S 17/89 (2020.01); G06T 7/292 (2017.01); G06T 7/246 (2017.01); G06T 7/70 (2017.01); G01S 17/931 (2020.01); G01S 17/86 (2020.01); B60W 30/095 (2012.01); G01S 17/66 (2006.01); G06F 18/24 (2023.01); G06F 18/25 (2023.01); B60W 50/00 (2006.01);
U.S. Cl.
CPC ...
G01S 17/89 (2013.01); B60W 30/0956 (2013.01); G01S 17/66 (2013.01); G01S 17/86 (2020.01); G01S 17/931 (2020.01); G06F 18/24 (2023.01); G06F 18/251 (2023.01); G06T 7/251 (2017.01); G06T 7/292 (2017.01); G06T 7/70 (2017.01); B60W 50/0097 (2013.01); B60W 2050/0028 (2013.01); B60W 2420/42 (2013.01); B60W 2420/52 (2013.01); B60W 2554/404 (2020.02); B60W 2556/35 (2020.02); G06T 2207/10016 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/30241 (2013.01); G06T 2207/30252 (2013.01);
Abstract

A surround multi-object tracking and surround vehicle motion prediction framework is provided. A full-surround camera array and LiDAR sensor based approach provides for multi-object tracking for autonomous vehicles. The multi-object tracking incorporates a fusion scheme to handle object proposals from the different sensors within the calibrated camera array. A motion prediction framework leverages the instantaneous motion of vehicles, an understanding of motion patterns of freeway traffic, and the effect of inter-vehicle interactions. The motion prediction framework incorporates probabilistic modeling of surround vehicle trajectories. Additionally, subcategorizing trajectories based on maneuver classes leads to better modeling of motion patterns. A model takes into account interactions between surround vehicles for simultaneously predicting each of their motion.


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