US 12,258,008 B1
Object collision path prediction
Thanard Kurutach, Bangkok (TH); Chenyi Chen, Belmont, CA (US); and Mircea Grecu, San Mateo, CA (US)
Assigned to GM Cruise Holdings LLC, San Francisco, CA (US)
Filed by GM Cruise Holdings LLC, San Francisco, CA (US)
Filed on Dec. 17, 2021, as Appl. No. 17/554,149.
Int. Cl. B60W 30/095 (2012.01); B60W 40/04 (2006.01); B60W 60/00 (2020.01); G06N 20/00 (2019.01); G06V 10/70 (2022.01); G06V 20/58 (2022.01)
CPC B60W 30/0956 (2013.01) [B60W 40/04 (2013.01); B60W 60/0011 (2020.02); B60W 60/00274 (2020.02); G06N 20/00 (2019.01); G06V 10/87 (2022.01); G06V 20/58 (2022.01)] 17 Claims
OG exemplary drawing
 
1. A method for identifying an object collision path, the method comprising:
operating an autonomous vehicle (AV) having a local computing device configured to execute a prediction stack and a planning stack, wherein the planning stack is configured to determine how to maneuver the AV within the environment:
receiving, by an object path prediction algorithm, sensor data indicative of objects in an environment, the sensor data being generated by sensors of the AV;
receiving, by the object path prediction algorithm, information about a location of the autonomous vehicle in the environment;
determining, by the object path prediction algorithm, a set of predicted paths for an object from the objects in the environment based on the sensor data;
outputting, from the object path prediction algorithm, selected paths from the set of predicted paths for the object, wherein the object path prediction algorithm is configured to output, to a planning algorithm, a fixed number of paths that are most likely to occur and a path that is considered an object collision path, the object collision path being a path that if taken by the object would result in the AV taking evasive action to avoid the object, wherein the object path prediction algorithm is a multimodal machine-learning prediction algorithm with a dedicated object collision head;
outputting, via the dedicated object collision head, the object collision path to the planning stack of the AV; and
executing the planning stack to control a maneuver of the AV based on the output object collision path.