公开(公告)号：US20220164350A1

公开(公告)日：2022-05-26

申请号：US17104921

申请日：2020-11-25

Applicant: Waymo LLC

Inventor： Jiyang Gao ,  Zijian Guo ,  Congcong Li ,  Xiaowei Li

IPC: G06F16/2458 ,  G06F16/2455 ,  G06F16/248 ,  G06K9/00 ,  G06N3/08 ,  B60W60/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for searching an autonomous vehicle sensor data repository. One of the methods includes maintaining a collection of sensor samples and one or more embeddings of each sensor sample. Each sensor sample is generated from sensor data at multiple time steps and characterizes an environment at each of the multiple time steps. Each embedding corresponds to a respective portion of the sensor sample and has been generated by an embedding neural network. A query specifying a query portion of a query sensor sample is received. A query embedding corresponding to the query portion of the query sensor sample is generated through the embedding neural network. A plurality of relevant sensor samples that have embeddings that are closest to the query embedding are identified as characterizing similar scenarios to the query portion of the query sensor sample.

公开(公告)号：US12067471B2

公开(公告)日：2024-08-20

申请号：US17104921

申请日：2020-11-25

Applicant: Waymo LLC

Inventor： Jiyang Gao ,  Zijian Guo ,  Congcong Li ,  Xiaowei Li

IPC: G06F16/24 ,  B60W60/00 ,  G06F30/27 ,  G06N3/02 ,  G06N3/08 ,  G06V20/56 ,  G08G1/01

CPC classification number: G06N3/02 ,  B60W60/0027 ,  G06F30/27 ,  G06N3/08 ,  G06V20/56 ,  G08G1/0104 ,  B60W2554/4045 ,  B60W2554/4046

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for searching an autonomous vehicle sensor data repository. One of the methods includes maintaining a collection of sensor samples and one or more embeddings of each sensor sample. Each sensor sample is generated from sensor data at multiple time steps and characterizes an environment at each of the multiple time steps. Each embedding corresponds to a respective portion of the sensor sample and has been generated by an embedding neural network. A query specifying a query portion of a query sensor sample is received. A query embedding corresponding to the query portion of the query sensor sample is generated through the embedding neural network. A plurality of relevant sensor samples that have embeddings that are closest to the query embedding are identified as characterizing similar scenarios to the query portion of the query sensor sample.