Machine-learning systems and techniques to optimize teleoperation and/or planner decisions
First Claim
1. A method comprising:
- receiving telemetry data from an autonomous vehicle, the telemetry data comprising sensor data from a sensor on the autonomous vehicle;
receiving policy data;
determining, based at least in part on at least one of the telemetry data or the policy data, an event in a region of an environment through which the autonomous vehicle has traversed;
determining, based at least in part on the event, that a state of operation of the autonomous vehicle is non-normative, the non-normative state indicating an insufficiency of guaranteeing collision-free travel;
determining, based at least in part on the state of operation, a plurality of trajectories;
determining, based at least in part on a trajectory of the plurality of trajectories, updated policy data; and
transmitting the updated policy data to the autonomous vehicle, the updated policy data configured to cause the autonomous vehicle to operate according to one or more policies of the updated policy data.
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Abstract
A system, an apparatus or a process may be configured to implement an application that applies artificial intelligence and/or machine-learning techniques to predict an optimal course of action (or a subset of courses of action) for an autonomous vehicle system (e.g., one or more of a planner of an autonomous vehicle, a simulator, or a teleoperator) to undertake based on suboptimal autonomous vehicle performance and/or changes in detected sensor data (e.g., new buildings, landmarks, potholes, etc.). The application may determine a subset of trajectories based on a number of decisions and interactions when resolving an anomaly due to an event or condition. The application may use aggregated sensor data from multiple autonomous vehicles to assist in identifying events or conditions that might affect travel (e.g., using semantic scene classification). An optimal subset of trajectories may be formed based on recommendations responsive to semantic changes (e.g., road construction).
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Citations
20 Claims
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1. A method comprising:
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receiving telemetry data from an autonomous vehicle, the telemetry data comprising sensor data from a sensor on the autonomous vehicle; receiving policy data; determining, based at least in part on at least one of the telemetry data or the policy data, an event in a region of an environment through which the autonomous vehicle has traversed; determining, based at least in part on the event, that a state of operation of the autonomous vehicle is non-normative, the non-normative state indicating an insufficiency of guaranteeing collision-free travel; determining, based at least in part on the state of operation, a plurality of trajectories; determining, based at least in part on a trajectory of the plurality of trajectories, updated policy data; and transmitting the updated policy data to the autonomous vehicle, the updated policy data configured to cause the autonomous vehicle to operate according to one or more policies of the updated policy data. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A system comprising:
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one or more processors; memory having stored thereon processor-executable instructions that, when executed by the one or more processors, cause the system to; receive telemetry data from an autonomous vehicle, the telemetry data comprising sensor data from a sensor on the autonomous vehicle; receive policy data; determine, based at least in part on at least one of the telemetry data or the policy data, a confidence level associated with an event in a region of an environment surrounding the autonomous vehicle; determine, based at least in part on the confidence level, a plurality of trajectories; determine, based at least in part on a trajectory of the plurality of trajectories, updated policy data; and transmit the updated policy data to the autonomous vehicle, the updated policy data configured to cause the autonomous vehicle to operate according to one or more policies of the updated policy data. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15)
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16. A system comprising:
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one or more processors; one or more sensors; a memory storing processor-executable instructions that, when executed by the one or more processors, cause the system to; receive telemetry data from the one or more sensors; receive policy data; determine, based at least in part on at least one of the telemetry data or the policy data, an event in a region of an environment surrounding an autonomous vehicle and a confidence level associated with the event; transmit, to a second device, at least part of at least one of the telemetry data or the policy data; receive, from the second device, updated policy data; alter, based at least in part on the updated policy data, operation of the autonomous vehicle during the event. - View Dependent Claims (17, 18, 19, 20)
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Specification