公开(公告)号：US20240353231A1

公开(公告)日：2024-10-24

申请号：US18304975

申请日：2023-04-21

申请人： Zoox, Inc.

发明人： Philippe Martin Burlina ,  Subhasis Das ,  Jackson Owen Waschura

IPC分类号： G01C21/32 ,  G06N20/20

CPC分类号： G01C21/32 ,  G06N20/20

摘要： A transformer-based machine-learned model may use cross-attention between map data and various sensor data and/or perception data, such as an object detection, to augment perception tasks. In particular, the transformer-based machine-learned model may comprise two or more encoders, one of which may determine a first embedding from map data and a second encoder that may determine a second embedding from sensor data and/or perception data. An encoder may determine a score that may be used to determine various outputs that may improve partially occluded object detection, ground plane classification, static object detection, and suppress false positive object detections.