US 12,282,328 B2
Systems and methods for using attention masks to improve motion planning
Raquel Urtasun, Toronto (CA); Bob Qingyuan Wei, Waterloo (CA); Mengye Ren, Toronto (CA); Wenyuan Zeng, Toronto (CA); Ming Liang, Toronto (CA); and Bin Yang, Toronto (CA)
Assigned to AURORA OPERATIONS, INC., Pittsburgh, PA (US)
Filed by Aurora Operations, Inc., Pittsburgh, PA (US)
Filed on Jan. 15, 2021, as Appl. No. 17/150,987.
Claims priority of provisional application 63/132,967, filed on Dec. 31, 2020.
Claims priority of provisional application 62/985,848, filed on Mar. 5, 2020.
Prior Publication US 2021/0278852 A1, Sep. 9, 2021
Int. Cl. G05D 1/00 (2024.01); B60W 60/00 (2020.01); G06F 18/213 (2023.01); G06N 20/00 (2019.01); G06T 17/05 (2011.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01)
CPC G05D 1/0217 (2013.01) [B60W 60/001 (2020.02); G05D 1/0214 (2013.01); G06F 18/213 (2023.01); G06N 20/00 (2019.01); G06T 17/05 (2013.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01)] 14 Claims
OG exemplary drawing
 
1. A computer-implemented method for improving autonomous vehicle motion planning, the method comprising:
accessing, by a computing system including one or more processors, sensor data and map data for an area around an autonomous vehicle:
generating, by the computing system, a voxel grid representation of the sensor data and the map data;
generating, by the computing system, an attention mask based on the voxel grid representation, wherein the attention mask is generated using a first machine-learned model trained to generate a grid of attention values, each of the attention values representing an importance of the map data to navigating the autonomous vehicle and wherein the importance of an attention value of the attention values is determined, at least in part, on a position of the respective attention value relative to the autonomous vehicle;
generating, by the computing system by using the voxel grid representation and the attention mask as input to a second machine-learned model, an attention weighted feature map;
determining, by the computing system, using the attention weighted feature map, a planning cost volume for the area around the autonomous vehicle;
selecting, by the computing system, a trajectory for the autonomous vehicle based, at least in part, on the planning cost volume; and
controlling, by the computing system, the autonomous vehicle based on the trajectory.