US 11,835,950 B2
Autonomous vehicle safe stop
Moslem Kazemi, Allison Park, PA (US); and Sameer Bardapurkar, Pittsburgh, PA (US)
Assigned to UATC, LLC, Mountain View, CA (US)
Filed by UATC, LLC, Mountain View, CA (US)
Filed on Feb. 22, 2021, as Appl. No. 17/181,733.
Application 17/181,733 is a continuation of application No. 15/995,285, filed on Jun. 1, 2018, granted, now 10,962,973.
Claims priority of provisional application 62/623,815, filed on Jan. 30, 2018.
Prior Publication US 2021/0271242 A1, Sep. 2, 2021
Int. Cl. G05D 1/00 (2006.01); B60W 30/09 (2012.01); B60W 30/095 (2012.01); G05D 1/02 (2020.01); B60T 7/22 (2006.01); B62D 1/28 (2006.01)
CPC G05D 1/0088 (2013.01) [B60T 7/22 (2013.01); B60W 30/09 (2013.01); B60W 30/0953 (2013.01); B60W 30/0956 (2013.01); B62D 1/286 (2013.01); G05D 1/0061 (2013.01); G05D 1/024 (2013.01); G05D 1/0246 (2013.01); G05D 2201/0213 (2013.01)] 22 Claims
OG exemplary drawing
 
1. A computer-implemented method of autonomous vehicle operation, the computer-implemented method comprising:
receiving, by a computing system comprising one or more computing devices, state data comprising information associated with at least one of: one or more states of an autonomous vehicle or one or more states of an environment external to the autonomous vehicle;
determining, by the computing system, that one or more vehicle stoppage conditions are satisfied based at least in part on the state data, wherein each of the one or more vehicle stoppage conditions is a condition that indicates that the autonomous vehicle is to stop traveling;
selecting, by the computing system based at least in part on the state data and a machine-learned model, a severity level for the one or more satisfied vehicle stoppage conditions from a plurality of available severity levels,
wherein the machine-learned model is trained to select the severity level based at least in part on the one or more satisfied vehicle stoppage conditions, the severity level indicating an immediacy with which the autonomous vehicle is to stop traveling;
generating, by the computing system, a motion plan based at least in part on the severity level; and
controlling, by the computing system, autonomous driving of the autonomous vehicle in accordance with the motion plan.