TRAJECTORY PREDICTION FROM MULTI-SENSOR FUSION

    公开(公告)号:US20250162618A1

    公开(公告)日:2025-05-22

    申请号:US18517750

    申请日:2023-11-22

    Applicant: Waymo LLC

    Abstract: Methods and systems for predicting a trajectory an autonomous vehicle (AV) are disclosed. A method includes generating, based on sensor data from a sensing system of the AV, one or more embeddings, generating, using a machine learning model (MLM) and the one or more embeddings, one or more predicted future trajectories for the AV, and causing, using the one or more predicted future trajectories, a planning system of the AV to generate an update to a current trajectory of the AV.

    CONTRASTIVE LEARNING FOR OBJECT DETECTION

    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

    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.

    Contrastive learning for object detection

    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

    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.

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