公开(公告)号：US20240190474A1

公开(公告)日：2024-06-13

申请号：US18202125

申请日：2023-05-25

申请人： PlusAI, Inc.

发明人： I-Hsuan Yang ,  Yu Wang

IPC分类号： B60W60/00

CPC分类号： B60W60/0027 ,  B60W2420/42 ,  B60W2420/52 ,  B60W2554/40

摘要： This application is directed to predicting vehicle actions according to a hierarchy of interconnected vehicle actions. The hierarchy of interconnected vehicle actions includes a plurality of predefined vehicle actions that are organized to define a plurality of vehicle action sequences. A first vehicle obtains one or more images of a road and a second vehicle, and predicts a sequence of vehicle actions of the second vehicle through the hierarchy of interconnected vehicle actions using the one or more images. The first vehicle is controlled to drive at least partially autonomously based on the predicted sequence of vehicle actions of the second vehicle. In some embodiments, the hierarchy of interconnected vehicle actions includes a first action level that is defined according to a stage of a trip and corresponds to three predefined vehicle actions of: “start a trip,” “move in a trip,” and “complete a trip.”

公开(公告)号：US11657280B1

公开(公告)日：2023-05-23

申请号：US17978491

申请日：2022-11-01

申请人： PlusAI, Inc.

发明人： Yue Guo ,  I-Hsuan Yang ,  Yu Wang

IPC分类号： G06N3/08

CPC分类号： G06N3/08

摘要： Embodiments provide reinforcement learning (RL) techniques for transferring machine learning obtained in one domain to another. A neural network (NN) of a source domain may be trained using training data associated with that domain. In some cases, the training data is inaccessible to a target domain for which a similar NN is desired. A number of target NNs may be initialized with a portion of the parameters transferred from the source NN. A computing agent may utilize RL techniques to train the target NNs using training data of the target domain. The target NN with the most beneficial subset of transferred parameters may be selected. By using the RL techniques to identify the most advantageous combination of transferred parameters, a similarly accurate NN may be provided in the target domain at a fraction of the otherwise needed time and without accessing the original training data.

公开(公告)号：US11926335B1

公开(公告)日：2024-03-12

申请号：US18214418

申请日：2023-06-26

申请人： PlusAI, Inc.

发明人： Yu Wang ,  Yongzuan Wu

IPC分类号： B60W50/06 ,  B60W30/18 ,  B60W60/00

CPC分类号： B60W50/06 ,  B60W30/18163 ,  B60W60/0027 ,  B60W2554/4045

摘要： Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining a symmetric scenario for a scenario; training a first machine learning model for the scenario based on first training data generated from second training data for the symmetric scenario; and generating a prediction for the scenario based on the first machine learning model.

公开(公告)号：US11697435B1

公开(公告)日：2023-07-11

申请号：US18078529

申请日：2022-12-09

申请人： PlusAI, Inc.

发明人： I-Hsuan Yang ,  Yu Wang

IPC分类号： B60W60/00

CPC分类号： B60W60/0027 ,  B60W2420/42 ,  B60W2420/52 ,  B60W2554/40

摘要： This application is directed to predicting vehicle actions according to a hierarchy of interconnected vehicle actions. The hierarchy of interconnected vehicle actions includes a plurality of predefined vehicle actions that are organized to define a plurality of vehicle action sequences. A first vehicle obtains one or more images of a road and a second vehicle, and predicts a sequence of vehicle actions of the second vehicle through the hierarchy of interconnected vehicle actions using the one or more images. The first vehicle is controlled to drive at least partially autonomously based on the predicted sequence of vehicle actions of the second vehicle. In some embodiments, the hierarchy of interconnected vehicle actions includes a first action level that is defined according to a stage of a trip and corresponds to three predefined vehicle actions of: “start a trip,” “move in a trip,” and “complete a trip.”