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:
Sep. 07, 2021

Filed:

Feb. 04, 2020
Applicant:

Nio Usa, Inc., San Jose, CA (US);

Inventors:

Huazeng Deng, San Jose, CA (US);

Ajaya H S Rao, San Jose, CA (US);

Ashwath Aithal, Fremont, CA (US);

Xu Chen, Livermore, CA (US);

Ruoyu Tan, Millbrae, CA (US);

Veera Ganesh Yalla, Sunnyvale, CA (US);

Assignee:

NIO USA, Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/62 (2006.01); G01S 13/86 (2006.01); G01S 7/41 (2006.01); G01S 7/40 (2006.01); G01S 13/42 (2006.01); G06K 9/46 (2006.01); G06T 7/80 (2017.01); G06T 7/70 (2017.01); G06T 7/20 (2017.01); G06T 7/62 (2017.01); G05D 1/02 (2020.01); G05D 1/00 (2006.01);
U.S. Cl.
CPC ...
G06K 9/629 (2013.01); G01S 7/40 (2013.01); G01S 7/41 (2013.01); G01S 7/417 (2013.01); G01S 13/42 (2013.01); G01S 13/865 (2013.01); G01S 13/867 (2013.01); G06K 9/00791 (2013.01); G06K 9/46 (2013.01); G06K 9/6267 (2013.01); G06T 7/20 (2013.01); G06T 7/62 (2017.01); G06T 7/70 (2017.01); G06T 7/80 (2017.01); G05D 1/0088 (2013.01); G05D 1/0231 (2013.01); G05D 1/0251 (2013.01); G05D 1/0257 (2013.01); G05D 2201/0213 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/10044 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30252 (2013.01);
Abstract

Embodiments of the present disclosure are directed to a method for object detection. The method includes receiving sensor data indicative of one or more objects for each of a camera subsystem, a LiDAR subsystem, and an imaging RADAR subsystem. The sensor data is received simultaneously and within one frame for each of the subsystems. The method also includes extracting one or more feature representations of the objects from camera image data, LiDAR point cloud data and imaging RADAR point cloud data and generating image feature maps, LiDAR feature maps and imaging RADAR feature maps. The method further includes combining the image feature maps, the LiDAR feature maps and the imaging RADAR feature maps to generate merged feature maps and generating object classification, object position, object dimensions, object heading and object velocity from the merged feature maps.


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