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1.发明申请公开(公告)号：US20210295171A1
公开(公告)日：2021-09-23
申请号：US16824199
申请日：2020-03-19
Applicant: NVIDIA Corporation
Inventor： Alexey Kamenev , Nikolai Smolyanskiy , Ishwar Kulkarni , Ollin Boer Bohan , Fangkai Yang , Alperen Degirmenci , Ruchi Bhargava , Urs Muller , David Nister , Rotem Aviv
Abstract: In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.
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公开(公告)号：US20240239374A1
公开(公告)日：2024-07-18
申请号：US18620096
申请日：2024-03-28
Applicant: NVIDIA Corporation
Inventor： David Nister , Yizhou Wang , Julia Ng , Rotem Aviv , Seungho Lee , Joshua John Bialkowski , Hon Leung Lee , Hermes Lanker , Raul Correal Tezanos , Zhenyi Zhang , Nikolai Smolyanskiy , Alexey Kamenev , Ollin Boer Bohan , Anton Vorontsov , Miguel Sainz Serra , Birgit Henke
CPC classification number: B60W60/0011 , B60W50/0097 , G06N3/08
Abstract: Embodiments of the present disclosure relate to behavior planning for autonomous vehicles. The technology described herein selects a preferred trajectory for an autonomous vehicle based on an evaluation of multiple hypothetical trajectories by different components within a planning system. The various components provide an optimization score for each trajectory according to the priorities of the component and scores from multiple components may form a final optimization score. This scoring system allows the competing priorities (e.g., comfort, minimal travel time, fuel economy) of different components to be considered together. In examples, the trajectory with the best combined score may be selected for implementation. As such, an iterative approach that evaluates various factors may be used to identify an optimal or preferred trajectory for an autonomous vehicle when navigating an environment.
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Patent Agency Ranking
公开(公告)号：US11981349B2
公开(公告)日：2024-05-14
申请号：US17178464
申请日：2021-02-18
Applicant: NVIDIA Corporation
Inventor： David Nister , Yizhou Wang , Julia Ng , Rotem Aviv , Seungho Lee , Joshua John Bialkowski , Hon Leung Lee , Hermes Lanker , Raul Correal Tezanos , Zhenyi Zhang , Nikolai Smolyanskiy , Alexey Kamenev , Ollin Boer Bohan , Anton Vorontsov , Miguel Sainz Serra , Birgit Henke
CPC classification number: B60W60/0011 , B60W50/0097 , G05D1/0212 , G06N3/08
Abstract: Embodiments of the present disclosure relate to behavior planning for autonomous vehicles. The technology described herein selects a preferred trajectory for an autonomous vehicle based on an evaluation of multiple hypothetical trajectories by different components within a planning system. The various components provide an optimization score for each trajectory according to the priorities of the component and scores from multiple components may form a final optimization score. This scoring system allows the competing priorities (e.g., comfort, minimal travel time, fuel economy) of different components to be considered together. In examples, the trajectory with the best combined score may be selected for implementation. As such, an iterative approach that evaluates various factors may be used to identify an optimal or preferred trajectory for an autonomous vehicle when navigating an environment.
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公开(公告)号：US12001958B2
公开(公告)日：2024-06-04
申请号：US16824199
申请日：2020-03-19
Applicant: NVIDIA Corporation
Inventor： Alexey Kamenev , Nikolai Smolyanskiy , Ishwar Kulkarni , Ollin Boer Bohan , Fangkai Yang , Alperen Degirmenci , Ruchi Bhargava , Urs Muller , David Nister , Rotem Aviv
Abstract: In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.
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5.发明公开公开(公告)号：US20230260136A1
公开(公告)日：2023-08-17
申请号：US17672402
申请日：2022-02-15
Applicant: NVIDIA Corporation
CPC classification number: G06T7/254 , G06V10/454 , G06T2207/10028 , G06T2207/20084 , G06V2201/07
Abstract: In various examples, systems and methods of the present disclosure detect and/or track objects in an environment using projection images generated from LiDAR. For example, a machine learning model—such as a deep neural network (DNN)—may be used to compute a motion mask indicative of motion corresponding to points representing objects in an environment. Various input channels may be provided as input to the machine learning model to compute a motion mask. One or more comparison images may be generated based on comparing depth values projected from a current range image to a coordinate space of a previous range image to depth values of the previous range image. The machine learning model may use the one or more projection images, the one or more comparison images, and/or the one or more range images to compute a motion mask and/or a motion vector output representation.
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公开(公告)号：US20220138568A1
公开(公告)日：2022-05-05
申请号：US17453055
申请日：2021-11-01
Applicant: NVIDIA Corporation
Inventor： Nikolai Smolyanskiy , Alexey Kamenev , Lirui Wang , David Nister , Ollin Boer Bohan , Ishwar Kulkarni , Fangkai Yang , Julia Ng , Alperen Degirmenci , Ruchi Bhargava , Rotem Aviv
Abstract: In various examples, reinforcement learning is used to train at least one machine learning model (MLM) to control a vehicle by leveraging a deep neural network (DNN) trained on real-world data by using imitation learning to predict movements of one or more actors to define a world model. The DNN may be trained from real-world data to predict attributes of actors, such as locations and/or movements, from input attributes. The predictions may define states of the environment in a simulator, and one or more attributes of one or more actors input into the DNN may be modified or controlled by the simulator to simulate conditions that may otherwise be unfeasible. The MLM(s) may leverage predictions made by the DNN to predict one or more actions for the vehicle.
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公开(公告)号：US20210253128A1
公开(公告)日：2021-08-19
申请号：US17178464
申请日：2021-02-18
Applicant: NVIDIA Corporation
Inventor： David Nister , Yizhou Wang , Julia Ng , Rotem Aviv , Seungho Lee , Joshua John Bialkowski , Hon Leung Lee , Hermes Lanker , Raul Correal Tezanos , Zhenyi Zhang , Nikolai Smolyanskiy , Alexey Kamenev , Ollin Boer Bohan , Anton Vorontsov , Miguel Sainz Serra , Birgit Henke
Abstract: Embodiments of the present disclosure relate to behavior planning for autonomous vehicles. The technology described herein selects a preferred trajectory for an autonomous vehicle based on an evaluation of multiple hypothetical trajectories by different components within a planning system. The various components provide an optimization score for each trajectory according to the priorities of the component and scores from multiple components may form a final optimization score. This scoring system allows the competing priorities (e.g., comfort, minimal travel time, fuel economy) of different components to be considered together. In examples, the trajectory with the best combined score may be selected for implementation. As such, an iterative approach that evaluates various factors may be used to identify an optimal or preferred trajectory for an autonomous vehicle when navigating an environment.
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