US 12,140,959 B2
Autonomous vehicle operation feature monitoring and evaluation of effectiveness
Blake Konrardy, San Francisco, CA (US); Scott T. Christensen, Salem, OR (US); Gregory Hayward, Bloomington, IL (US); and Scott Farris, Bloomington, IL (US)
Assigned to STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY, Bloomington, IL (US)
Filed by STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY, Bloomington, IL (US)
Filed on Jan. 3, 2023, as Appl. No. 18/149,488.
Application 18/149,488 is a continuation of application No. 16/817,845, filed on Mar. 13, 2020, granted, now 11,580,604.
Application 16/817,845 is a continuation of application No. 15/421,521, filed on Feb. 1, 2017, granted, now 10,599,155, issued on Mar. 24, 2020.
Application 15/421,521 is a continuation in part of application No. 14/713,249, filed on May 15, 2015, granted, now 10,529,027.
Claims priority of provisional application 62/291,789, filed on Feb. 5, 2016.
Claims priority of provisional application 62/056,893, filed on Sep. 29, 2014.
Claims priority of provisional application 62/047,307, filed on Sep. 8, 2014.
Claims priority of provisional application 62/035,878, filed on Aug. 11, 2014.
Claims priority of provisional application 62/035,832, filed on Aug. 11, 2014.
Claims priority of provisional application 62/035,780, filed on Aug. 11, 2014.
Claims priority of provisional application 62/035,769, filed on Aug. 11, 2014.
Claims priority of provisional application 62/035,660, filed on Aug. 11, 2014.
Claims priority of provisional application 62/036,090, filed on Aug. 11, 2014.
Claims priority of provisional application 62/035,867, filed on Aug. 11, 2014.
Claims priority of provisional application 62/035,723, filed on Aug. 11, 2014.
Claims priority of provisional application 62/035,980, filed on Aug. 11, 2014.
Claims priority of provisional application 62/035,669, filed on Aug. 11, 2014.
Claims priority of provisional application 62/035,729, filed on Aug. 11, 2014.
Claims priority of provisional application 62/035,859, filed on Aug. 11, 2014.
Claims priority of provisional application 62/035,983, filed on Aug. 11, 2014.
Claims priority of provisional application 62/018,169, filed on Jun. 27, 2014.
Claims priority of provisional application 62/000,878, filed on May 20, 2014.
Prior Publication US 2023/0143946 A1, May 11, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/00 (2023.01); B60W 10/04 (2006.01); B60W 10/20 (2006.01); B60W 30/09 (2012.01); B60W 30/18 (2012.01); G05D 1/00 (2006.01); G06N 20/00 (2019.01); G06Q 40/08 (2012.01)
CPC G05D 1/0221 (2013.01) [B60W 10/04 (2013.01); B60W 10/20 (2013.01); B60W 30/09 (2013.01); B60W 30/18163 (2013.01); G05D 1/0088 (2013.01); G06N 20/00 (2019.01); G06Q 40/08 (2013.01); B60W 2420/40 (2013.01); B60W 2420/403 (2013.01); B60W 2420/408 (2024.01); B60W 2540/00 (2013.01); B60W 2552/00 (2020.02); B60W 2554/00 (2020.02); B60W 2710/20 (2013.01); B60W 2720/10 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer system for evaluating operation of an autonomous operation feature for controlling vehicle operation, comprising:
one or more processors; and
a non-transitory program memory coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to:
receive indications of a plurality of vehicle collisions involving a plurality of vehicles having the autonomous operation feature;
for each vehicle collision of the plurality of vehicle collisions involving a respective vehicle of the plurality of vehicles:
receive sensor data from one or more sensors within the vehicle indicating (i) one or more environmental conditions in which the vehicle collision occurred, (ii) a person positioned within the vehicle to operate the vehicle at the time of the vehicle collision, and (iii) one or more capabilities or features of the autonomous operation feature of the vehicle;
determine one or more preferred control decisions the autonomous operation feature could have made to control the vehicle to reduce a risk of collision or mitigate an effect of the vehicle collision immediately before or during the vehicle collision based upon analysis of the sensor data using a trained machine learning program that has been previously trained to predict preferred control decisions under a plurality of operating conditions associated with corresponding sets of training sensor data;
receive control decision data indicating one or more actual control decisions the autonomous operation feature of the vehicle made to control the vehicle immediately before or during the vehicle collision; and
assign a degree of fault for the vehicle collision to the autonomous operation feature based upon an extent of consistency or inconsistency between
the one or more preferred control decisions and the one or more actual control decisions; and
determine a risk level for the autonomous operation feature based upon the respective degrees of fault for the plurality of vehicle collisions.