US 11,927,668 B2
Radar deep learning
Daniel Hendricus Franciscus Fontijne, Haarlem (NL); Amin Ansari, San Diego, CA (US); Bence Major, Amsterdam (NL); Ravi Teja Sukhavasi, La Jolla, CA (US); Radhika Dilip Gowaikar, San Diego, CA (US); Xinzhou Wu, San Diego, CA (US); Sundar Subramanian, San Diego, CA (US); and Michael John Hamilton, San Diego, CA (US)
Assigned to QUALCOMM Incorporated, San Diego, CA (US)
Filed by QUALCOMM Incorporated, San Diego, CA (US)
Filed on Nov. 27, 2019, as Appl. No. 16/698,870.
Claims priority of provisional application 62/774,018, filed on Nov. 30, 2018.
Prior Publication US 2021/0255304 A1, Aug. 19, 2021
Int. Cl. G01S 13/60 (2006.01); G01S 7/02 (2006.01); G01S 7/41 (2006.01); G01S 13/931 (2020.01); G01S 17/931 (2020.01); G06V 10/764 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06V 20/58 (2022.01); G06V 20/70 (2022.01); G01S 7/295 (2006.01); G01S 13/86 (2006.01); G01S 13/89 (2006.01); G01S 17/89 (2020.01); G06F 18/2413 (2023.01); G06F 18/25 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01)
CPC G01S 13/931 (2013.01) [G01S 7/022 (2013.01); G01S 7/417 (2013.01); G01S 13/60 (2013.01); G01S 17/931 (2020.01); G06V 10/764 (2022.01); G06V 10/803 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06V 20/58 (2022.01); G06V 20/70 (2022.01); G01S 7/2955 (2013.01); G01S 13/865 (2013.01); G01S 13/867 (2013.01); G01S 13/89 (2013.01); G01S 17/89 (2013.01); G06F 18/24133 (2023.01); G06F 18/251 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A method of employing deep learning to analyze radar signals performed by an on-board computer of a host vehicle, comprising:
receiving, from a radar sensor of the host vehicle, a plurality of radar frames;
executing a neural network on at least a subset of the plurality of radar frames, wherein Light Detection And Ranging (LiDAR) data is concatenated with raw radar data into a single input tensor prior to executing the neural network on the subset of the plurality of radar frames; and
detecting one or more objects in the subset of the plurality of radar frames based on execution of the neural network on the subset of the plurality of radar frames.