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公开(公告)号：US20250042437A1
公开(公告)日：2025-02-06
申请号：US18798488
申请日：2024-08-08
Applicant: Aurora Operations, Inc.
Inventor： James Andrew Bagnell , Sanjiban Choudhury , Michael Lee Phillips , Arun Venkatraman , Xinyan Yan
Abstract: An example method includes obtaining log data descriptive of an exemplar action of an exemplar vehicle in an environment, the exemplar action occurring in an initial state of the environment; determining, using the operational system, a planned action for a simulated vehicle in the initial state of the environment; simulating an SUT state of the environment resulting from the simulated vehicle executing the planned action in the initial state of the environment and an actor performing an actor action subsequent to the planned action and an exemplar state of the environment resulting from the simulated vehicle executing the exemplar action in the initial state of the environment and the actor performing the actor action subsequent to the exemplar action; determining a test score based on the SUT state and a reference score based on the exemplar state; evaluating the operational system based on the test score and the reference score.
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公开(公告)号：US12151707B1
公开(公告)日：2024-11-26
申请号：US18633191
申请日：2024-04-11
Applicant: Aurora Operations, Inc.
Inventor： James Andrew Bagnell , Brian Christopher Becker , Davis Edward King , Skandavimal Shridhar , Drew Edward Steedly , Xinyan Yan
IPC: B60W60/00
Abstract: The present disclosure provides an example method for validating a trajectory generated by an autonomous vehicle control system (AV trajectory) in a driving scenario. The example method includes (a) obtaining the AV trajectory and a reference trajectory, wherein the reference trajectory describes a desired motion of a vehicle in the driving scenario; (b) determining a plurality of component divergence values for a plurality of divergence metrics, wherein a respective divergence value characterizes a respective difference between the AV trajectory and the reference trajectory; (c) providing the plurality of component divergence values to a machine-learned model to generate a score that indicates an aggregate divergence between the AV trajectory and the reference trajectory, wherein the machine-learned model comprises a plurality of learned parameters defining an influence of the plurality of component divergence values on the score; and (d) validating the AV trajectory based on the score.
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公开(公告)号：US20240317261A1
公开(公告)日：2024-09-26
申请号：US18309494
申请日：2023-04-28
Applicant: Aurora Operations, Inc.
Inventor： James Andrew Bagnell , Sanjiban Choudhury , Michael Lee Phillips , Arun Venkatraman , Xinyan Yan
CPC classification number: B60W60/0015 , B60W40/09
Abstract: An example method includes obtaining log data descriptive of an exemplar action of an exemplar vehicle in an environment, the exemplar action occurring in an initial state of the environment; determining, using the operational system, a planned action for a simulated vehicle in the initial state of the environment; simulating an SUT state of the environment resulting from the simulated vehicle executing the planned action in the initial state of the environment and an actor performing an actor action subsequent to the planned action and an exemplar state of the environment resulting from the simulated vehicle executing the exemplar action in the initial state of the environment and the actor performing the actor action subsequent to the exemplar action; determining a test score based on the SUT state and a reference score based on the exemplar state; evaluating the operational system based on the test score and the reference score.
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公开(公告)号：US12084085B1
公开(公告)日：2024-09-10
申请号：US18309494
申请日：2023-04-28
Applicant: Aurora Operations, Inc.
Inventor： James Andrew Bagnell , Sanjiban Choudhury , Michael Lee Phillips , Arun Venkatraman , Xinyan Yan
CPC classification number: B60W60/0015 , B60W40/09
Abstract: An example method includes obtaining log data descriptive of an exemplar action of an exemplar vehicle in an environment, the exemplar action occurring in an initial state of the environment; determining, using the operational system, a planned action for a simulated vehicle in the initial state of the environment; simulating an SUT state of the environment resulting from the simulated vehicle executing the planned action in the initial state of the environment and an actor performing an actor action subsequent to the planned action and an exemplar state of the environment resulting from the simulated vehicle executing the exemplar action in the initial state of the environment and the actor performing the actor action subsequent to the exemplar action; determining a test score based on the SUT state and a reference score based on the exemplar state; evaluating the operational system based on the test score and the reference score.
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