US 11,851,015 B2
In-cabin hazard prevention and safety control system for autonomous machine applications
Atousa Torabi, Santa Clara, CA (US); Sakthivel Sivaraman, Santa Clara, CA (US); Niranjan Avadhanam, Saratoga, CA (US); and Shagan Sah, Santa Clara, CA (US)
Assigned to NVIDIA Corporation, Santa Clara, CA (US)
Filed by NVIDIA Corporation, Santa Clara, CA (US)
Filed on Sep. 7, 2022, as Appl. No. 17/939,622.
Application 17/939,622 is a continuation of application No. 16/915,577, filed on Jun. 29, 2020, granted, now 11,485,308.
Prior Publication US 2023/0001872 A1, Jan. 5, 2023
Int. Cl. B60R 21/017 (2006.01); B60R 21/013 (2006.01); B60W 60/00 (2020.01); G06N 3/02 (2006.01); B60W 50/14 (2020.01); B60W 50/00 (2006.01); B60R 21/01 (2006.01)
CPC B60R 21/017 (2013.01) [B60R 21/013 (2013.01); B60W 50/14 (2013.01); B60W 60/005 (2020.02); G06N 3/02 (2013.01); B60R 2021/01211 (2013.01); B60R 2021/01286 (2013.01); B60W 2050/0062 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
determining, using one or more neural networks and based at least on image data representative of an image depicting an occupant of an autonomous or semi-autonomous machine, one or more three-dimensional (3D) key point locations within a volume of space enclosed by the autonomous or semi-autonomous machine that are associated with the occupant;
determining, based at least on the one or more 3D key point locations, an activity associated with the occupant; and
performing, based at least on the activity associated with the occupant, one or more operations associated with the autonomous or semi-autonomous machine.