公开(公告)号：US11756309B2

公开(公告)日：2023-09-12

申请号：US17148148

申请日：2021-01-13

Applicant: Waymo LLC

Inventor： Alper Ayvaci ,  Feiyu Chen ,  Justin Yu Zheng ,  Bayram Safa Cicek ,  Vasiliy Igorevich Karasev

IPC: G06N3/08 ,  G06V20/58 ,  B60W60/00

CPC classification number: G06V20/58 ,  B60W60/001 ,  G06N3/08 ,  B60W2420/52 ,  B60W2554/4049

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using contrastive learning. One of the methods includes obtaining a network input representing an environment; processing the network input using a first subnetwork of the neural network to generate a respective embedding for each location in the environment; processing the embeddings for each location in the environment using a second subnetwork of the neural network to generate a respective object prediction for each location; determining, for each of a plurality of pairs of the plurality of locations in the environment, whether the respective object predictions of the pair of locations characterize the same possible object or different possible objects; computing a respective contrastive loss value for each of the plurality of pairs of locations; and updating values for a plurality of parameters of the first subnetwork using the computed contrastive loss values.

公开(公告)号：US20230249712A1

公开(公告)日：2023-08-10

申请号：US17669215

申请日：2022-02-10

Applicant: Waymo LLC

Inventor： Xu Chen ,  Chao-Yeh Chen ,  Justin Yu Zheng ,  Zhinan Xu

IPC: B60W60/00 ,  G01S13/931 ,  G01S17/931 ,  G05B13/02 ,  G06N3/04 ,  G06V20/58 ,  G06V10/764 ,  G06V10/82

CPC classification number: B60W60/0015 ,  G01S13/931 ,  G01S17/931 ,  G05B13/027 ,  G06N3/0445 ,  G06N3/0454 ,  G06V20/58 ,  G06V10/764 ,  G06V10/82 ,  B60W2420/42 ,  B60W2420/52 ,  B60W2420/403 ,  B60W2554/4026 ,  B60W2554/4029

Abstract: The described aspects and implementations enable efficient calibration of a sensing system of a vehicle. In one implementation, disclosed is a method and a system to perform the method, the system including the sensing system configured to collect sensing data, characterizing an environment of the vehicle, the sensing data including infrared sensing data. The system further includes a data processing system operatively coupled to the sensing system and configured to process the sensing data using a classifier machine-learning model to obtain a classification of one or more vulnerable road users present in the environment of the vehicle.

公开(公告)号：US20220164585A1

公开(公告)日：2022-05-26

申请号：US17148148

申请日：2021-01-13

Applicant: Waymo LLC

Inventor： Alper Ayvaci ,  Feiyu Chen ,  Justin Yu Zheng ,  Bayram Safa Cicek ,  Vasiliy Igorevich Karasev

IPC: G06K9/00 ,  G06N3/08 ,  B60W60/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using contrastive learning. One of the methods includes obtaining a network input representing an environment; processing the network input using a first subnetwork of the neural network to generate a respective embedding for each location in the environment; processing the embeddings for each location in the environment using a second subnetwork of the neural network to generate a respective object prediction for each location; determining, for each of a plurality of pairs of the plurality of locations in the environment, whether the respective object predictions of the pair of locations characterize the same possible object or different possible objects; computing a respective contrastive loss value for each of the plurality of pairs of locations; and updating values for a plurality of parameters of the first subnetwork using the computed contrastive loss values.