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US 12,679,409 B2Granted
Parameter space optimization
AssigneeZoox, Inc.
InventorsGerrit Bagschik, Andraz Kavalar
Life of this patent
Estimating the term…
Abstract
Techniques for analyzing a parameter space are discussed. Techniques may include receiving policy data for evaluating a vehicle controller. The techniques may further include determining, using a Bayesian optimization and based at least in part on the vehicle controller, parameter sets associated with adverse events. The adverse events may be associated with a violation of the policy data. The techniques may associate, based on exposure data, parameter bounds of the adverse events and probabilities of the adverse events in a driving environment. A safety metric may be determined based on the Bayesian optimization. The techniques may also include weighting an impact of an adverse event based on the safety metric.
Claims20
A method comprising:
- receiving policy data for evaluating a vehicle controller;
- determining, using a Bayesian optimization and based at least in part on the vehicle controller, parameter sets associated with adverse events, wherein the adverse events are associated with a violation of the policy data, wherein parameters in the parameter sets represent conditions of the vehicle controller and an object in a simulated environment, determined using the Bayesian optimization and based on the vehicle controller, resulting in an increased likelihood of a simulation resulting in the adverse events between the vehicle controller and the object;
- associating, based on exposure data, bounds of the parameters of the parameters sets of the adverse events and probabilities of the adverse events in a driving environment, wherein the bounds of the parameters in the parameter sets include a minimum value and a maximum value of a distance, a velocity, or an acceleration;
- determining a ratio between first results associated with first parameter value ranges indicating an adverse event occurred and second results associated with second parameter value ranges indicating a lack of the adverse event;
- determining a safety metric based on the Bayesian optimization and the ratio;
- and
- assigning a safety metric value to the adverse event.
4 more elements
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 system to perform operations comprising:
- receiving policy data for evaluating a vehicle controller;
- determining, using a Bayesian optimization and based at least in part on the vehicle controller, parameter sets associated with adverse events, wherein the adverse events are associated with a violation of the policy data, wherein parameters in the parameter sets represent conditions of the vehicle controller and an object in a simulated environment, determined using the Bayesian optimization and based on the vehicle controller, resulting in an increased likelihood of a simulation resulting in the adverse events between the vehicle controller and the object;
- associating, based on exposure data, bounds of the parameters in the parameter sets of the adverse events and probabilities of the adverse events in a driving environment, wherein the bounds of parameters in the parameter sets include a minimum value and a maximum value of a distance, a velocity, or an acceleration;
- determining a ratio between first results associated with first parameter value ranges indicating an adverse event occurred and second results associated with second parameter value ranges indicating a lack of the adverse event;
- determining, based at least in part on the ratio, a safety metric;
- assigning a safety metric value to the adverse event;
- and
- determining whether the safety metric exceeds a threshold value for use in an autonomous vehicle.
8 more elements
One or more non-transitory computer-readable media storing instructions that, when executed, cause one or more processors to perform operations comprising:
- receiving policy data for evaluating a vehicle controller;
- determining, using a Bayesian optimization and based at least in part on the vehicle controller, parameter sets associated with adverse events, wherein the adverse events are associated with a violation of the policy data, wherein parameters in the parameter sets represent conditions of the vehicle controller and an object in a simulated environment, determined using the Bayesian optimization and based on the vehicle controller, resulting in an increased likelihood of a simulation resulting in the adverse events between the vehicle controller and the object;
- associating, based on exposure data, bounds of the parameters in the parameter sets of the adverse events and probabilities of the adverse events in a driving environment, wherein the bounds of the parameters in the parameter sets include a minimum value and a maximum value of a distance, a velocity, and an acceleration;
- determining a ratio between first results associated with first parameter value ranges indicating an adverse event occurred and second results associated with second parameter value ranges indicating a lack of the adverse event;
- determining a safety metric based on the Bayesian optimization and the ratio;
- and
- assigning a safety metric value to the adverse event.
4 more elements
Dependencies are inferred from the claim text.