US 11,745,758 B2
Method and system for context-aware decision making of an autonomous agent
Gautam Narang, Palo Alto, CA (US); Apeksha Kumavat, Palo Alto, CA (US); Arjun Narang, Palo Alto, CA (US); Kinh Tieu, Palo Alto, CA (US); Michael Smart, Palo Alto, CA (US); and Marko Ilievski, Palo Alto, CA (US)
Assigned to Gatik AI Inc., Palo Alto, CA (US)
Filed by Gatik AI Inc., Palo Alto, CA (US)
Filed on Jun. 22, 2022, as Appl. No. 17/846,870.
Application 17/846,870 is a continuation of application No. 17/584,062, filed on Jan. 25, 2022, granted, now 11,396,307.
Application 17/584,062 is a continuation of application No. 17/332,839, filed on May 27, 2021, granted, now 11,260,882, issued on Mar. 1, 2022.
Application 17/332,839 is a continuation of application No. 17/306,014, filed on May 3, 2021, granted, now 11,267,485, issued on Mar. 8, 2022.
Application 17/306,014 is a continuation of application No. 17/116,810, filed on Dec. 9, 2020, granted, now 11,034,364, issued on Jun. 15, 2021.
Claims priority of provisional application 63/055,756, filed on Jul. 23, 2020.
Claims priority of provisional application 63/035,401, filed on Jun. 5, 2020.
Prior Publication US 2022/0315042 A1, Oct. 6, 2022
Int. Cl. B60W 60/00 (2020.01); G01C 21/34 (2006.01); G01C 21/36 (2006.01); H04W 4/021 (2018.01); G06N 20/00 (2019.01); G06F 18/214 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/70 (2022.01); G06V 20/56 (2022.01)
CPC B60W 60/001 (2020.02) [G01C 21/3461 (2013.01); G01C 21/3673 (2013.01); G06F 18/2155 (2023.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/87 (2022.01); G06V 20/56 (2022.01); H04W 4/021 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A method for an autonomous vehicle, comprising:
based on a set of sensor measurements collected with a sensor suite of the autonomous vehicle, determining a vehicle state and a situational condition associated with the vehicle state,
based on the situational condition, selecting a deep learning model from a predetermined plurality of deep learning models;
with the deep learning model, determining a vehicle behavior;
determining a command for the autonomous vehicle based on the vehicle behavior; and
facilitating operation of the autonomous vehicle based on the command.