US 11,994,868 B2
Autonomous vehicle routing based upon spatiotemporal factors
Antony Joseph, San Francisco, CA (US); Geoffrey Louis Chi-Johnston, San Francisco, CA (US); Vishal Suresh Vaingankar, Kensington, CA (US); and Laura Athena Freeman, San Francisco, CA (US)
Assigned to GM Cruise Holdings LLC, San Francisco, CA (US)
Filed by GM Cruise Holdings LLC, San Francisco, CA (US)
Filed on Jan. 17, 2023, as Appl. No. 18/097,561.
Application 18/097,561 is a continuation of application No. 16/280,415, filed on Feb. 20, 2019, granted, now 11,561,547.
Prior Publication US 2023/0152813 A1, May 18, 2023
Int. Cl. G05D 1/00 (2006.01); G01C 21/20 (2006.01); G01C 21/34 (2006.01); G01C 21/36 (2006.01)
CPC G05D 1/0214 (2013.01) [G01C 21/20 (2013.01); G01C 21/3484 (2013.01); G01C 21/3691 (2013.01); G05D 1/0011 (2013.01); G05D 1/0061 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computing system comprising:
a processor, and
memory that stores computer-readable instructions that, when executed by the processor,
cause the processor to perform acts comprising:
receiving an origin location and a destination location of an autonomous vehicle; and
identifying a route for the autonomous vehicle from the origin location to the destination location based upon output of a computer-implemented spatiotemporal statistical model that is generated based upon historical data from autonomous vehicles having undergone operation-influencing events in a driving environment, an operation-influencing event comprising detection by a particular autonomous vehicle of an object within a threshold distance of the particular autonomous vehicle, wherein the historical data comprises indications of geographic locations traversed by the autonomous vehicles, indications of spatiotemporal factors in the geographic locations when the autonomous vehicles undergo the operation-influencing events, and times at which the autonomous vehicles encountered the spatiotemporal factors, wherein the spatiotemporal factors comprise conditions in the driving environment that interfere with at least one of object recognition by the autonomous vehicle or physical progress of the autonomous vehicle.