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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
Inventor: Yu Ouyang , Daniel Ho , Alper Ayvaci , Tencia Lee , Bayram Safa Cicek
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.
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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
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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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
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.
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