Reinforcement learning for traffic simulation
AssigneeNVIDIA Corporation
InventorsYulong Cao, Chaowei Xiao, Marco Pavone, Boris Ivanovic
Life of this patent
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Abstract
In various examples, a traffic model including one or more traffic scenarios may be generated and/or updated based on using human feedback. Human feedback may be provided indicating a preference for various traffic scenarios to identify which scenarios in a model are more realistic. A reward model may capture the preference information and rank the realism of one or more traffic scenarios.
Claims20
A computer-implemented method comprising:
- accessing one or more traffic scenes of a traffic model;
- accessing preference data indicating a realism of the one or more traffic scenes;
- calculating a reward value based, at least in part, on the preference data;
- updating the traffic model using the reward value;
- and
- moving an autonomous vehicle based on updating the traffic model.
A non-transitory computer readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:
- access one or more traffic scenes of a traffic model;
- access preference data indicating a realism of the one or more traffic scenes;
- calculate a reward value based, at least in part, on the preference data;
- and
- update the traffic model using the reward value.
A system comprising:
- one or more processors to:
- access one or more traffic scenes of a traffic model;
- access preference data indicating a realism of the one or more traffic scenes;
- calculate a reward value, using one or more neural network, based, at least in part, on the preference data;
- and
- update the traffic model using the reward value.
Dependencies are inferred from the claim text.