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公开(公告)号：US20240300525A1
公开(公告)日：2024-09-12
申请号：US18269209
申请日：2021-12-17
CPC分类号： B60W60/001 , G06N20/00 , B60W2554/4026 , B60W2554/4029 , B60W2556/10
摘要： Systems and methods related to controlling an autonomous vehicle (“AV”) are described herein. Implementations can process actor(s) from a past episode of locomotion of a vehicle, and stream(s) in an environment of the vehicle during the past episode to generate predicted output(s). The actor(s) may each be associated with a corresponding object in the environment of the vehicle, and the stream(s) may each represent candidate navigation paths in the environment of the vehicle. Further, implementations can process the predicted output(s) to generate further predicted output(s), and can compare the predicted output(s) to associated reference label(s). The processing can be performed utilizing layer(s) or distinct, additional layer(s) of machine learning (“ML”) model(s). Implementations can update the layer(s) or the additional layer(s) based on the comparing, and subsequently use the ML model(s) in controlling the AV.
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公开(公告)号：US20240217558A1
公开(公告)日：2024-07-04
申请号：US18471690
申请日：2023-09-21
IPC分类号： B60W60/00
CPC分类号： B60W60/00272 , B60W2554/4045 , B60W2556/00 , G06Q50/30
摘要： Example aspects of the present disclosure relate to an example computer-implemented method for predicting the intent of actors within an environment. The example method includes obtaining state data associated with a plurality of actors within the environment and map data indicating a plurality of lanes of the environment. The method includes determining a plurality of potential goals each actor based on the state data and the map data. The method includes processing the state data, the map data, and the plurality of potential goals with a machine-learned forecasting model to determine (i) a forecasted goal for a respective actor of the plurality of actors, (ii) a forecasted interaction between the respective actor and a different actor of the plurality of actors based on the forecasted goal, and (iii) a continuous trajectory for the respective actor based on the forecasted goal.
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公开(公告)号：US11952015B2
公开(公告)日：2024-04-09
申请号：US17522031
申请日：2021-11-09
CPC分类号： B60W60/0027 , B60W50/00 , B60W60/0011 , G06N20/00 , G06V20/584
摘要： Implementations process, using machine learning (ML) layer(s) of ML model(s), actor(s) from a past episode of locomotion of a vehicle and stream(s) in an environment of the vehicle during the past episode to forecast associated trajectories, for the vehicle and for each of the actor(s), with respect to a respective associated stream of the stream(s). Further, implementations process, using a stream connection function, the associated trajectories to forecast a plurality of associated trajectories, for the vehicle and each of the actor(s), with respect to each of the stream(s). Moreover, implementations iterate between using the ML layer(s) and the stream connection function to update the associated trajectories for the vehicle and each of the actor(s). Implementations subsequently use the ML layer(s) in controlling an AV.
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公开(公告)号：US20240043037A1
公开(公告)日：2024-02-08
申请号：US18269200
申请日：2021-12-17
CPC分类号： B60W60/0011 , B60W60/0027 , G06N3/044 , G06N3/084 , B60W2554/80 , B60W2556/10 , B60W2554/4041
摘要： Systems and methods related to controlling an autonomous vehicle (“AV”) are described herein. Implementations can obtain a plurality of instances that each include input and output. The input can include actor(s) from a given time instance of a past episode of locomotion of a vehicle, and stream(s) in an environment of the vehicle during the past episode. The actor(s) may be associated with an object in the environment of the vehicle at the given time instance, and the stream(s) may each represent candidate navigation paths in the environment of the vehicle. The output may include ground truth label(s) (or reference label(s)). Implementations can train a machine learning (“ML”) model based on the plurality of instances, and subsequently use the ML model in controlling the AV. In training the ML model, the actor(s) and stream(s) can be processed in parallel.
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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
发明人： James Andrew Bagnell , Sanjiban Choudhury , Michael Lee Phillips , Arun Venkatraman , Xinyan Yan
CPC分类号： B60W60/0015 , B60W40/09
摘要： 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
发明人： James Andrew Bagnell , Sanjiban Choudhury , Michael Lee Phillips , Arun Venkatraman , Xinyan Yan
CPC分类号： B60W60/0015 , B60W40/09
摘要： 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：US20240190477A1
公开(公告)日：2024-06-13
申请号：US18582149
申请日：2024-02-20
CPC分类号： B60W60/0027 , B60W50/00 , B60W60/0011 , G06N20/00 , G06V20/584
摘要： Implementations process, using machine learning (ML) layer(s) of ML model(s), actor(s) from a past episode of locomotion of a vehicle and stream(s) in an environment of the vehicle during the past episode to forecast associated trajectories, for the vehicle and for each of the actor(s), with respect to a respective associated stream of the stream(s). Further, implementations process, using a stream connection function, the associated trajectories to forecast a plurality of associated trajectories, for the vehicle and each of the actor(s), with respect to each of the stream(s). Moreover, implementations iterate between using the ML layer(s) and the stream connection function to update the associated trajectories for the vehicle and each of the actor(s). Implementations subsequently use the ML layer(s) in controlling an AV.
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公开(公告)号：US11801871B1
公开(公告)日：2023-10-31
申请号：US18147316
申请日：2022-12-28
CPC分类号： B60W60/00272 , B60W2552/53 , B60W2554/4041 , B60W2554/4044 , B60W2554/4045 , B60W2556/00 , G06Q50/30
摘要： Example aspects of the present disclosure relate to an example computer-implemented method for predicting the intent of actors within an environment. The example method includes obtaining state data associated with a plurality of actors within the environment and map data indicating a plurality of lanes of the environment. The method include determining a plurality of potential goals each actor based on the state data and the map data. The method includes processing the state data, the map data, and the plurality of potential goals with a machine-learned forecasting model to determine (i) a forecasted goal for a respective actor of the plurality of actors, (ii) a forecasted interaction between the respective actor and a different actor of the plurality of actors based on the forecasted goal, and (iii) a continuous trajectory for the respective actor based on the forecasted goal.
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公开(公告)号：US20230145236A1
公开(公告)日：2023-05-11
申请号：US17522031
申请日：2021-11-09
CPC分类号： B60W60/0027 , B60W60/0011 , B60W50/00 , G06N20/00 , G06K9/00825
摘要： Implementations process, using machine learning (ML) layer(s) of ML model(s), actor(s) from a past episode of locomotion of a vehicle and stream(s) in an environment of the vehicle during the past episode to forecast associated trajectories, for the vehicle and for each of the actor(s), with respect to a respective associated stream of the stream(s). Further, implementations process, using a stream connection function, the associated trajectories to forecast a plurality of associated trajectories, for the vehicle and each of the actor(s), with respect to each of the stream(s). Moreover, implementations iterate between using the ML layer(s) and the stream connection function to update the associated trajectories for the vehicle and each of the actor(s). Implementations subsequently use the ML layer(s) in controlling an AV.
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