Training a codebook for trajectory determination
AssigneeZoox, Inc.
InventorEthan Miller Pronovost
Life of this patent
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Abstract
Techniques for training a codebook usable by a machine learned model to predict an object trajectory or scene data are described herein. For example, the techniques may include generating tokens representing discrete object behavior into a machine learned model that outputs a sequence of tokens that is usable by another machine learned model to generate the object trajectory (e.g., position data, velocity data, acceleration data, etc.) or the scene data associated with the environment. The object trajectory can be sent to a vehicle computing device for consideration during vehicle planning, which may include simulation.
Claims20
A system comprising:
- one or more processors;
- and
- one or more non-transitory computer-readable media storing instructions executable by the one or more processors, wherein the instructions, when executed, cause the one or more processors to perform actions comprising:
- receiving, by a training component, state data representing a previous state of an object in an environment;
- receiving, by the training component, feature vectors representing a vehicle and the object in the environment;
- training a codebook as a trained codebook, the training comprising:
- assigning, based at least in part on the state data, a first token to represent the previous state of the object;
- assigning a second token to represent a characteristic of the vehicle;
- assigning a third token to represent a feature of the environment;
- and
- mapping the feature vectors to respective tokens;
- and
- outputting the trained codebook for use by a machine learned model configured to access tokens from the trained codebook and to arrange the first token, the second token, and the third token to represent a potential interaction between the vehicle and the object.
A method comprising:
- receiving, by a training component, state data representing a previous state of an object or a vehicle in an environment;
- receiving, by the training component, feature vectors representing the vehicle and the object in the environment;
- training a codebook to output a trained codebook, the training comprising:
- assigning, based at least in part on the state data, a first token to represent the previous state of the object;
- assigning a second token to represent a characteristic of the vehicle;
- assigning a third token to represent a feature of the environment;
- and
- mapping the feature vectors to respective tokens;
- and
- outputting the trained codebook for use by a machine learned model configured to access tokens from the trained codebook and to arrange the first token, the second token, and the third token to represent a potential interaction between the vehicle and the object.
One or more non-transitory computer-readable media storing instructions that, when executed, cause one or more processors to perform actions comprising:
- receiving, by a training component, state data representing a previous state of an object or a vehicle in an environment;
- receiving, by the training component, feature vectors representing the vehicle and the object in the environment;
- training a codebook to output a trained codebook, the training comprising:
- assigning, based at least in part on the state data, a first token to represent the previous state of the object;
- assigning a second token to represent a characteristic of the vehicle;
- assigning a third token to represent a feature of the environment;
- and
- mapping the feature vectors to respective tokens;
- and
- outputting the trained codebook for use by a machine learned model configured to access tokens from the trained codebook and to arrange the first token, the second token, and the third token to represent a potential interaction between the vehicle and the object.
Dependencies are inferred from the claim text.