Machine learning for predicting locations of objects perceived by autonomous vehicles
First Claim
1. A computer system, comprising:
- one or more processors;
a scenario generation system implemented by the one or more processors, wherein the scenario generation system is configured to;
receive state data descriptive of at least a current or past state of an object that is perceived by an autonomous vehicle; and
generate one or more goals for the object based at least in part on the state data, wherein each of the one or more goals corresponds to a set of one or more decisions that result in the object achieving a goal location;
wherein the scenario generation system comprises a machine-learned goal scoring model configured to generate a score for each of the one or more goals generated for the object; and
wherein the score generated by the machine-learned goal scoring model for each goal is indicative of a predicted likelihood that the object will pursue the set of one or more decisions associated with such goal to achieve the goal location associated with such goal; and
a scenario development system implemented by the one or more processors,wherein at least one of the scenario generation system and the scenario development system is further configured to select at least one of the one or more goals based at least in part on the scores generated for the one or more goals by the machine-learned goal scoring model;
wherein the scenario development system is configured to;
receive data indicative of the at least one of the one or more goals selected by the at least one of the scenario generation system and the scenario development system; and
determine at least one predicted trajectory by which the object achieves the at least one goal location associated with the at least one of the one or more goals selected by the at least one of the scenario generation system and the scenario development system; and
wherein the scenario development system comprises a machine-learned trajectory scoring model configured to provide at least one score for the at least one predicted trajectory by which the object achieves the at least one goal location associated with the at least one of the one or more goals, wherein a score provided by the machine-learned trajectory scoring model for a particular predicted trajectory is indicative of a predicted likelihood that the object would move along the particular trajectory if attempted by the object, and wherein the machine-learned trajectory scoring model has been trained on training data that comprises a first set of training trajectories labelled as valid trajectories and a second set of training trajectories labelled as invalid trajectories.
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Abstract
The present disclosure provides systems and methods for predicting the future locations of objects that are perceived by autonomous vehicles. An autonomous vehicle can include a prediction system that, for each object perceived by the autonomous vehicle, generates one or more potential goals, selects one or more of the potential goals, and develops one or more trajectories by which the object can achieve the one or more selected goals. The prediction systems and methods described herein can include or leverage one or more machine-learned models that assist in predicting the future locations of the objects. As an example, in some implementations, the prediction system can include a machine-learned static object classifier, a machine-learned goal scoring model, a machine-learned trajectory development model, a machine-learned ballistic quality classifier, and/or other machine-learned models. The use of machine-learned models can improve the speed, quality, and/or accuracy of the generated predictions.
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Citations
19 Claims
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1. A computer system, comprising:
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one or more processors; a scenario generation system implemented by the one or more processors, wherein the scenario generation system is configured to; receive state data descriptive of at least a current or past state of an object that is perceived by an autonomous vehicle; and generate one or more goals for the object based at least in part on the state data, wherein each of the one or more goals corresponds to a set of one or more decisions that result in the object achieving a goal location; wherein the scenario generation system comprises a machine-learned goal scoring model configured to generate a score for each of the one or more goals generated for the object; and wherein the score generated by the machine-learned goal scoring model for each goal is indicative of a predicted likelihood that the object will pursue the set of one or more decisions associated with such goal to achieve the goal location associated with such goal; and a scenario development system implemented by the one or more processors, wherein at least one of the scenario generation system and the scenario development system is further configured to select at least one of the one or more goals based at least in part on the scores generated for the one or more goals by the machine-learned goal scoring model; wherein the scenario development system is configured to; receive data indicative of the at least one of the one or more goals selected by the at least one of the scenario generation system and the scenario development system; and determine at least one predicted trajectory by which the object achieves the at least one goal location associated with the at least one of the one or more goals selected by the at least one of the scenario generation system and the scenario development system; and wherein the scenario development system comprises a machine-learned trajectory scoring model configured to provide at least one score for the at least one predicted trajectory by which the object achieves the at least one goal location associated with the at least one of the one or more goals, wherein a score provided by the machine-learned trajectory scoring model for a particular predicted trajectory is indicative of a predicted likelihood that the object would move along the particular trajectory if attempted by the object, and wherein the machine-learned trajectory scoring model has been trained on training data that comprises a first set of training trajectories labelled as valid trajectories and a second set of training trajectories labelled as invalid trajectories. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. An autonomous vehicle, comprising:
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one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising; obtaining state data descriptive of at least one current or past state of an object that is perceived by the autonomous vehicle; generating one or more goals for the object based at least in part on the state data, wherein each of the one or more goals corresponds to a set of one or more decisions that result in the object achieving a goal location; selecting at least a first goal of the one or more goals generated for the object, wherein selecting at least the first goal of the one or more of the goals generated for the object comprises; inputting at least the state data descriptive of the at least one current or past state of the object and data descriptive of the one or more goals generated for the object into a machine-learned goal scoring model implemented by the one or more processors; receiving one or more scores for the object respectively relative to the one or more goals as an output of the machine-learned goal scoring model, wherein the score output by the machine-learned goal scoring model for each goal is indicative of a predicted likelihood that the object will pursue the set of one or more decisions associated with such goal to achieve the goal location associated with such goal; and selecting at least the first goal from the one or more goals based at least in part on the one or more scores; determining at least a first predicted trajectory by which the object achieves a first goal location associated with the first goal; inputting at least the first predicted trajectory into a machine-learned trajectory scoring model configured to provide at least a first score for at least the first predicted trajectory, wherein the trajectory scoring model has been trained on training data that comprises a first set of training trajectories labelled as valid trajectories and a second set of training trajectories labelled as invalid trajectories; and receiving the least the first score for at least the first predicted trajectory as an output of the machine-learned trajectory scoring model, wherein the first score is indicative of a predicted likelihood that the object would move along the first predicted trajectory if attempted by the object. - View Dependent Claims (16, 17, 18)
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19. A computer-implemented method, comprising:
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obtaining, by a computing system comprising one or more computing devices, state data descriptive of at least one current or past state of an object that is perceived by an autonomous vehicle; generating, by the computing system, one or more goals for the object based at least in part on the state data, wherein each of the one or more goals corresponds to a set of one or more decisions that result in the object achieving a goal location; selecting, by the computing system, at least a first goal of the one or more goals generated for the object, wherein selecting, by the computing system, at least the first goal of the one or more of the goals generated for the object comprises; inputting, by the computing system, at least the state data descriptive of the at least one current or past state of the object and data descriptive of the one or more goals generated for the object into a machine-learned goal scoring model implemented by the one or more processors; receiving, by the computing system, one or more scores for the object respectively relative to the one or more goals as an output of the machine-learned goal scoring model, wherein the score generated by the machine-learned goal scoring model for each goal is indicative of a predicted likelihood that the object will pursue the set of one or more decisions associated with such goal to achieve the goal location associated with such goal; and selecting, by the computing system, at least the first goal from the one or more goals based at least in part on the one or more scores; determining, by the computing system, at least a first predicted trajectory by which the object achieves the first goal; inputting at least the first predicted trajectory into a machine-learned trajectory scoring model configured to provide at least a first score for at least the first predicted trajectory, wherein the trajectory scoring model has been trained on training data that comprises a first set of training trajectories labelled as valid trajectories and a second set of training trajectories labelled as invalid trajectories; and receiving the least the first score for at least the first predicted trajectory as an output of the machine-learned trajectory scoring model, wherein the first score is indicative of a predicted likelihood that the object would move along the first predicted trajectory if attempted by the object.
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Specification