公开(公告)号：US11755025B2

公开(公告)日：2023-09-12

申请号：US18153072

申请日：2023-01-11

Applicant: NVIDIA Corporation

Inventor： Chenyi Chen ,  Artem Provodin ,  Urs Muller

IPC: G06N3/08 ,  G05D1/02 ,  G05D1/00 ,  B60W30/18 ,  G06N20/00 ,  B62D15/02 ,  B60W30/00 ,  G06N3/045 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56

CPC classification number: G05D1/0221 ,  B60W30/00 ,  B60W30/18154 ,  B62D15/02 ,  B62D15/025 ,  B62D15/0255 ,  G05D1/0088 ,  G06N3/045 ,  G06N3/08 ,  G06N20/00 ,  G06V10/764 ,  G06V10/82 ,  G06V20/588 ,  B60W2420/42 ,  B60W2420/52 ,  G05D2201/0213

Abstract: In various examples, a trigger signal may be received that is indicative of a vehicle maneuver to be performed by a vehicle. A recommended vehicle trajectory for the vehicle maneuver may be determined in response to the trigger signal being received. To determine the recommended vehicle trajectory, sensor data may be received that represents a field of view of at least one sensor of the vehicle. A value of a control input and the sensor data may then be applied to a machine learning model(s) and the machine learning model(s) may compute output data that includes vehicle control data that represents the recommended vehicle trajectory for the vehicle through at least a portion of the vehicle maneuver. The vehicle control data may then be sent to a control component of the vehicle to cause the vehicle to be controlled according to the vehicle control data.

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

IPC: G06N3/08 ,  G06N3/04

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.

公开(公告)号：US20210271254A1

公开(公告)日：2021-09-02

申请号：US17322365

申请日：2021-05-17

Applicant: NVIDIA Corporation

Inventor： Chenyi Chen ,  Artem Provodin ,  Urs Muller

IPC: G05D1/02 ,  G05D1/00 ,  B60W30/18 ,  G06N20/00 ,  B62D15/02 ,  G06N3/08 ,  G06N3/04 ,  B60W30/00

Abstract: In various examples, a trigger signal may be received that is indicative of a vehicle maneuver to be performed by a vehicle. A recommended vehicle trajectory for the vehicle maneuver may be determined in response to the trigger signal being received. To determine the recommended vehicle trajectory, sensor data may be received that represents a field of view of at least one sensor of the vehicle. A value of a control input and the sensor data may then be applied to a machine learning model(s) and the machine learning model(s) may compute output data that includes vehicle control data that represents the recommended vehicle trajectory for the vehicle through at least a portion of the vehicle maneuver. The vehicle control data may then be sent to a control component of the vehicle to cause the vehicle to be controlled according to the vehicle control data.

公开(公告)号：US20250069385A1

公开(公告)日：2025-02-27

申请号：US18945136

申请日：2024-11-12

Applicant: NVIDIA Corporation

Inventor： Haiguang Wen ,  Bernhard Firner ,  Mariusz Bojarski ,  Zongyi Yang ,  Urs Muller

IPC: G06V10/82 ,  G06N3/08 ,  G06T7/70 ,  G06T9/00 ,  G06V10/25 ,  G06V10/50 ,  G06V10/52 ,  G06V20/56

Abstract: In examples, image data representative of an image of a field of view of at least one sensor may be received. Source areas may be defined that correspond to a region of the image. Areas and/or dimensions of at least some of the source areas may decrease along at least one direction relative to a perspective of the at least one sensor. A downsampled version of the region (e.g., a downsampled image or feature map of a neural network) may be generated from the source areas based at least in part on mapping the source areas to cells of the downsampled version of the region. Resolutions of the region that are captured by the cells may correspond to the areas of the source areas, such that certain portions of the region (e.g., portions at a far distance from the sensor) retain higher resolution than others.

公开(公告)号：US20240183752A1

公开(公告)日：2024-06-06

申请号：US18442753

申请日：2024-02-15

Applicant: NVIDIA Corporation

Inventor： Jesse Hong ,  Urs Muller ,  Bernhard Firner ,  Zongyi Yang ,  Joyjit Daw ,  David Nister ,  Roberto Giuseppe Luca Valenti ,  Rotem Aviv

IPC: G01M17/007 ,  B60W30/08 ,  B60W30/12 ,  B60W30/14 ,  B60W50/00 ,  B60W50/04 ,  B60W60/00 ,  G06F11/36 ,  G06V10/774 ,  G06V20/56 ,  G07C5/08

CPC classification number: G01M17/007 ,  B60W30/08 ,  B60W30/12 ,  B60W30/143 ,  B60W50/04 ,  B60W50/045 ,  B60W60/0011 ,  G06V10/774 ,  G06V20/56 ,  G07C5/08 ,  B60W2050/0028 ,  G06F11/3684 ,  G06F11/3696

Abstract: In various examples, sensor data recorded in the real-world may be leveraged to generate transformed, additional, sensor data to test one or more functions of a vehicle—such as a function of an AEB, CMW, LDW, ALC, or ACC system. Sensor data recorded by the sensors may be augmented, transformed, or otherwise updated to represent sensor data corresponding to state information defined by a simulation test profile for testing the vehicle function(s). Once a set of test data has been generated, the test data may be processed by a system of the vehicle to determine the efficacy of the system with respect to any number of test criteria. As a result, a test set including additional or alternative instances of sensor data may be generated from real-world recorded sensor data to test a vehicle in a variety of test scenarios.

公开(公告)号：US20240127062A1

公开(公告)日：2024-04-18

申请号：US18533860

申请日：2023-12-08

Applicant: NVIDIA Corporation

Inventor： Urs Muller ,  Mariusz Bojarski ,  Chenyi Chen ,  Bernhard Firner

IPC: G06N3/08 ,  G06N20/00 ,  G06V10/774 ,  G06V20/56

CPC classification number: G06N3/08 ,  G06N20/00 ,  G06V10/774 ,  G06V20/56

Abstract: In various examples, a machine learning model—such as a deep neural network (DNN)—may be trained to use image data and/or other sensor data as inputs to generate two-dimensional or three-dimensional trajectory points in world space, a vehicle orientation, and/or a vehicle state. For example, sensor data that represents orientation, steering information, and/or speed of a vehicle may be collected and used to automatically generate a trajectory for use as ground truth data for training the DNN. Once deployed, the trajectory points, the vehicle orientation, and/or the vehicle state may be used by a control component (e.g., a vehicle controller) for controlling the vehicle through a physical environment. For example, the control component may use these outputs of the DNN to determine a control profile (e.g., steering, decelerating, and/or accelerating) specific to the vehicle for controlling the vehicle through the physical environment.

公开(公告)号：US20230168683A1

公开(公告)日：2023-06-01

申请号：US18153072

申请日：2023-01-11

Applicant: NVIDIA Corporation

Inventor： Chenyi Chen ,  Artem Provodin ,  Urs Muller

IPC: G05D1/02 ,  G05D1/00 ,  B60W30/18 ,  G06N20/00 ,  B62D15/02 ,  G06N3/08 ,  B60W30/00 ,  G06N3/045 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56

CPC classification number: G05D1/0221 ,  G05D1/0088 ,  B60W30/18154 ,  G06N20/00 ,  B62D15/0255 ,  B62D15/025 ,  G06N3/08 ,  B62D15/02 ,  B60W30/00 ,  G06N3/045 ,  G06V10/764 ,  G06V10/82 ,  G06V20/588 ,  G05D2201/0213 ,  B60W2420/52 ,  B60W2420/42

Abstract: In various examples, a trigger signal may be received that is indicative of a vehicle maneuver to be performed by a vehicle. A recommended vehicle trajectory for the vehicle maneuver may be determined in response to the trigger signal being received. To determine the recommended vehicle trajectory, sensor data may be received that represents a field of view of at least one sensor of the vehicle. A value of a control input and the sensor data may then be applied to a machine learning model(s) and the machine learning model(s) may compute output data that includes vehicle control data that represents the recommended vehicle trajectory for the vehicle through at least a portion of the vehicle maneuver. The vehicle control data may then be sent to a control component of the vehicle to cause the vehicle to be controlled according to the vehicle control data.

公开(公告)号：US20230110713A1

公开(公告)日：2023-04-13

申请号：US17497479

申请日：2021-10-08

Applicant: NVIDIA Corporation

Inventor： Alperen Degirmenci ,  Won Hong ,  Mariusz Bojarski ,  Jesper Eduard van Engelen ,  Bernhard Firner ,  Zongyi Yang ,  Urs Muller

IPC: G05D1/02 ,  G06N3/00 ,  B60W60/00

Abstract: In various examples, a plurality of poses corresponding to one or more configuration parameters within an environment—such as a location of a machine within an environment, an orientation of a machine within an environment, a sensor angle pose of a machine, or a sensor location of a machine—may be used to generate training data and corresponding ground truth data for training a machine learning model—such as a deep neural network (DNN). As a result, the machine learning model, once deployed, may more accurately compute one or more outputs—such as outputs representative of lane boundaries, trajectories for an autonomous machine, etc.—agnostic to machine and/or sensor poses of the machine within which the machine learning model is deployed.

公开(公告)号：US20190212749A1

公开(公告)日：2019-07-11

申请号：US16241005

申请日：2019-01-07

Applicant: NVIDIA Corporation

Inventor： Chenyi Chen ,  Artem Provodin ,  Urs Muller

IPC: G05D1/02 ,  G05D1/00 ,  B60W30/18 ,  G06N20/00

CPC classification number: G05D1/0221 ,  B60W30/00 ,  B60W30/18154 ,  B60W2420/42 ,  B60W2420/52 ,  B62D15/02 ,  B62D15/025 ,  B62D15/0255 ,  G05D1/0088 ,  G05D2201/0213 ,  G06N3/0454 ,  G06N3/08 ,  G06N20/00

Abstract: In various examples, a trigger signal may be received that is indicative of a vehicle maneuver to be performed by a vehicle. A recommended vehicle trajectory for the vehicle maneuver may be determined in response to the trigger signal being received. To determine the recommended vehicle trajectory, sensor data may be received that represents a field of view of at least one sensor of the vehicle. A value of a control input and the sensor data may then be applied to a machine learning model(s) and the machine learning model(s) may compute output data that includes vehicle control data that represents the recommended vehicle trajectory for the vehicle through at least a portion of the vehicle maneuver. The vehicle control data may then be sent to a control component of the vehicle to cause the vehicle to be controlled according to the vehicle control data.

公开(公告)号：US20230359213A1

公开(公告)日：2023-11-09

申请号：US18355148

申请日：2023-07-19

Applicant: NVIDIA Corporation

Inventor： Chenyi Chen ,  Artem Provodin ,  Urs Muller

IPC: G05D1/02 ,  G05D1/00 ,  B60W30/18 ,  G06N20/00 ,  B62D15/02 ,  G06N3/08 ,  B60W30/00 ,  G06N3/045 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56

CPC classification number: G05D1/0221 ,  G05D1/0088 ,  B60W30/18154 ,  G06N20/00 ,  B62D15/0255 ,  B62D15/025 ,  G06N3/08 ,  B62D15/02 ,  B60W30/00 ,  G06N3/045 ,  G06V10/764 ,  G06V10/82 ,  G06V20/588 ,  G05D2201/0213 ,  B60W2420/52 ,  B60W2420/42

Abstract: In various examples, a trigger signal may be received that is indicative of a vehicle maneuver to be performed by a vehicle. A recommended vehicle trajectory for the vehicle maneuver may be determined in response to the trigger signal being received. To determine the recommended vehicle trajectory, sensor data may be received that represents a field of view of at least one sensor of the vehicle. A value of a control input and the sensor data may then be applied to a machine learning model(s) and the machine learning model(s) may compute output data that includes vehicle control data that represents the recommended vehicle trajectory for the vehicle through at least a portion of the vehicle maneuver. The vehicle control data may then be sent to a control component of the vehicle to cause the vehicle to be controlled according to the vehicle control data.