Scene generation using probabilistic programming languages
AssigneeWaymo LLC
InventorCongrui Hetang
Life of this patent
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Abstract
A system includes a memory device, and a processing device, operatively coupled to the memory device, to receive a set of input data including a roadgraph and a distribution of a plurality of scene types associated with a driving context, the roadgraph including an autonomous vehicle driving path, perform stochastic sampling based on the distribution of the plurality of scene types to obtain a set of synthetic scenes for the driving context, and train a machine learning model to identify, for each synthetic scene of the set of synthetic scenes, a respective modified autonomous vehicle driving path.
Claims20
A system comprising:
- a memory device;
- and
- a processing device, operatively coupled to the memory device, to:
- receive a set of input data including a roadgraph and a first distribution of a plurality of scene types associated with a driving context, the roadgraph representing an autonomous vehicle driving path, and the first distribution of the plurality of scene types comprising the plurality of scene types each associated with a weight indicating a probability of an occurrence of a respective scene type;
- generate a set of synthetic scenes for the driving context by performing stochastic sampling based on the first distribution of the plurality of scene types, wherein the processing device is to place a set of artifacts to define a synthetic scene within the roadgraph based on a second distribution of a variable associated with requirements for the driving context;
- train a machine learning model to identify, for each synthetic scene of the set of synthetic scenes, a respective modified autonomous vehicle driving path to be navigated by an autonomous vehicle;
- and
- cause an autonomous vehicle control system to modify, using the trained machine learning model, the autonomous driving path.
A method comprising:
- receiving, by a processing device, a set of input data including a roadgraph and a first distribution of a plurality of scene types associated with a driving context, the roadgraph representing an autonomous vehicle driving path, and the first distribution of the plurality of scene types comprising the plurality of scene types each associated with a weight indicating a probability of an occurrence of a respective scene type;
- generating, by the processing device, a set of synthetic scenes for the driving context by performing stochastic sampling based on the first distribution of the plurality of scene types, wherein the processing device is to place a set of artifacts to define a synthetic scene within the roadgraph based on a second distribution of a variable associated with requirements for the driving context;
- training, by the processing device, a machine learning model to identify, for each synthetic scene of the set of synthetic scenes, a respective modified autonomous vehicle driving path to be navigated by an autonomous vehicle;
- and
- causing an autonomous vehicle control system to modify, using the trained machine learning model, the autonomous driving path.
A non-transitory computer-readable storage medium having instructions stored thereon that, when executed by a processing device, cause the processing device to:
- receive a set of input data including a roadgraph and a first distribution of a plurality of scene types associated with a driving context, the roadgraph representing an autonomous vehicle driving path, and the first distribution of the plurality of scene types comprising the plurality of scene types each associated with a weight indicating a probability of an occurrence of a respective scene type;
- generate a set of synthetic scenes for the driving context by performing stochastic sampling based on the first distribution of the plurality of scene types, wherein the processing device is to place a set of artifacts to define a synthetic scene within the roadgraph based on a second distribution of a variable associated with requirements for the driving context;
- train a machine learning model to identify, for each synthetic scene of the set of synthetic scenes, a respective modified autonomous vehicle driving path to be navigated by an autonomous vehicle;
- and
- cause an autonomous vehicle control system to modify, using the trained machine learning model, the autonomous driving path.
Dependencies are inferred from the claim text.