公开(公告)号：US20250138534A1

公开(公告)日：2025-05-01

申请号：US19011224

申请日：2025-01-06

Applicant: NVIDIA Corporation

Inventor： David Nister ,  Hon-Leung Lee ,  Julia Ng ,  Yizhou Wang

IPC: G05D1/00 ,  B60W30/09 ,  B60W30/095 ,  G05D1/245 ,  G05D1/247 ,  G05D1/248 ,  G05D1/617 ,  G05D1/65 ,  G05D1/693 ,  G06N3/08

Abstract: In various examples, a current claimed set of points representative of a volume in an environment occupied by a vehicle at a time may be determined. A vehicle-occupied trajectory and at least one object-occupied trajectory may be generated at the time. An intersection between the vehicle-occupied trajectory and an object-occupied trajectory may be determined based at least in part on comparing the vehicle-occupied trajectory to the object-occupied trajectory. Based on the intersection, the vehicle may then execute the first safety procedure or an alternative procedure that, when implemented by the vehicle when the object implements the second safety procedure, is determined to have a lesser likelihood of incurring a collision between the vehicle and the object than the first safety procedure.

公开(公告)号：US12286115B2

公开(公告)日：2025-04-29

申请号：US17116138

申请日：2020-12-09

Applicant: NVIDIA Corporation

Inventor： Trung Pham ,  Berta Rodriguez Hervas ,  Minwoo Park ,  David Nister ,  Neda Cvijetic

IPC: B60W30/18 ,  G06N3/04 ,  G06N3/08 ,  G06T7/33 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56 ,  G06V20/64 ,  G06N3/045

Abstract: In various examples, a three-dimensional (3D) intersection structure may be predicted using a deep neural network (DNN) based on processing two-dimensional (2D) input data. To train the DNN to accurately predict 3D intersection structures from 2D inputs, the DNN may be trained using a first loss function that compares 3D outputs of the DNN—after conversion to 2D space—to 2D ground truth data and a second loss function that analyzes the 3D predictions of the DNN in view of one or more geometric constraints—e.g., geometric knowledge of intersections may be used to penalize predictions of the DNN that do not align with known intersection and/or road structure geometries. As such, live perception of an autonomous or semi-autonomous vehicle may be used by the DNN to detect 3D locations of intersection structures from 2D inputs.

公开(公告)号：US12248319B2

公开(公告)日：2025-03-11

申请号：US18340255

申请日：2023-06-23

Applicant: NVIDIA Corporation

Inventor： Minwoo Park ,  Xiaolin Lin ,  Hae-Jong Seo ,  David Nister ,  Neda Cvijetic

IPC: G05D1/00 ,  G05D1/228 ,  G06F18/214 ,  G06F18/23 ,  G06F18/2411 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08 ,  G06V10/14 ,  G06V10/44 ,  G06V10/48 ,  G06V10/75 ,  G06V10/764 ,  G06V10/766 ,  G06V10/776 ,  G06V10/82 ,  G06V10/94 ,  G06V20/56

Abstract: In various examples, systems and methods are disclosed that preserve rich spatial information from an input resolution of a machine learning model to regress on lines in an input image. The machine learning model may be trained to predict, in deployment, distances for each pixel of the input image at an input resolution to a line pixel determined to correspond to a line in the input image. The machine learning model may further be trained to predict angles and label classes of the line. An embedding algorithm may be used to train the machine learning model to predict clusters of line pixels that each correspond to a respective line in the input image. In deployment, the predictions of the machine learning model may be used as an aid for understanding the surrounding environment—e.g., for updating a world model—in a variety of autonomous machine applications.

公开(公告)号：US12202518B2

公开(公告)日：2025-01-21

申请号：US17497685

申请日：2021-10-08

Applicant: NVIDIA Corporation

Inventor： Julia Ng ,  Sachin Pullaikudi Veedu ,  David Nister ,  Hanne Buur ,  Hans Jonas Nilsson ,  Hon Leung Lee ,  Yunfei Shi ,  Charles Jerome Vorbach, Jr.

IPC: B60W60/00 ,  B60W30/095 ,  G06F9/50

Abstract: In various examples, a safety decomposition architecture for autonomous machine applications is presented that uses two or more individual safety assessments to satisfy a higher safety integrity level (e.g., ASIL D). For example, a behavior planner may be used as a primary planning component, and a collision avoidance feature may be used as a diverse safety monitoring component—such that both may redundantly and independently prevent violation of safety goals. In addition, robustness of the system may be improved as single point and systematic failures may be avoided due to the requirement that two independent failures—e.g., of the behavior planner component and the collision avoidance component—occur simultaneously to cause a violation of the safety goals.

公开(公告)号：US12179795B2

公开(公告)日：2024-12-31

申请号：US17328052

申请日：2021-05-24

Applicant: NVIDIA Corporation

Inventor： Birgit Henke ,  David Nister ,  Julia Ng

IPC: B60W60/00

Abstract: A trajectory for an autonomous machine may be evaluated for safety based at least on determining whether the autonomous machine would be capable of occupying points of the trajectory in space-time while still being able to avoid a potential future collision with one or more objects in the environment through use of one or more safety procedures. To do so, a point of the trajectory may be evaluated for conflict based at least on a comparison between points in space-time that correspond to the autonomous machine executing the safety procedure(s) from the point and arrival times of the one or more objects to corresponding position(s) in the environment. A trajectory may be sampled and evaluated for conflicts at various points throughout the trajectory. Based on results of one or more evaluations, the trajectory may be scored, eliminated from consideration, or otherwise considered for control of the autonomous machine.

公开(公告)号：US20240320986A1

公开(公告)日：2024-09-26

申请号：US18734354

申请日：2024-06-05

Applicant: NVIDIA Corporation

Inventor： Mehmet Kocamaz ,  Neeraj Sajjan ,  Sangmin Oh ,  David Nister ,  Junghyun Kwon ,  Minwoo Park

IPC: G06V20/58 ,  G06N3/08 ,  G06V10/20 ,  G06V10/94 ,  G06V20/56 ,  G06V20/64

CPC classification number: G06V20/58 ,  G06N3/08 ,  G06V10/255 ,  G06V10/95 ,  G06V20/588 ,  G06V20/64

Abstract: In various examples, live perception from sensors of an ego-machine may be leveraged to detect objects and assign the objects to bounded regions (e.g., lanes or a roadway) in an environment of the ego-machine in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as output segmentation masks—that may correspond to a combination of object classification and lane identifiers. The output masks may be post-processed to determine object to lane assignments that assign detected objects to lanes in order to aid an autonomous or semi-autonomous machine in a surrounding environment.

公开(公告)号：US12077190B2

公开(公告)日：2024-09-03

申请号：US16877127

申请日：2020-05-18

Applicant: NVIDIA Corporation

Inventor： Julia Ng ,  David Nister ,  Zhenyi Zhang ,  Yizhou Wang

IPC: B60W60/00 ,  B60W30/095

CPC classification number: B60W60/00272 ,  B60W30/0953 ,  B60W60/0011 ,  B60W60/0018 ,  B60W2554/4041 ,  B60W2554/4042 ,  B60W2554/80

Abstract: In various examples, systems and methods are disclosed for weighting one or more optional paths based on obstacle avoidance or other safety considerations. In some embodiments, the obstacle avoidance considerations may be computed using a comparison of trajectories representative of safety procedures at present and future projected time steps of an ego-vehicle and other actors to ensure that each actor is capable of implementing their respective safety procedure while avoiding collisions at any point along the trajectory. This comparison may include filtering out a path(s) of an actor at a time step(s)—e.g., using a one-dimensional lookup—based on spatial relationships between the actor and the ego-vehicle at the time step(s). Where a particular path—or point along the path—does not satisfy a collision-free standard, the path may be penalized more negatively with respect to the obstacle avoidance considerations, or may be removed from consideration as a potential path.

公开(公告)号：US20240232616A9

公开(公告)日：2024-07-11

申请号：US18343291

申请日：2023-06-28

Applicant: NVIDIA Corporation

Inventor： Yilin Yang ,  Bala Siva Sashank Jujjavarapu ,  Pekka Janis ,  Zhaoting Ye ,  Sangmin Oh ,  Minwoo Park ,  Daniel Herrera Castro ,  Tommi Koivisto ,  David Nister

IPC: G06N3/08 ,  B60W30/14 ,  B60W60/00 ,  G06F18/214 ,  G06V10/762 ,  G06V20/56

CPC classification number: G06N3/08 ,  B60W30/14 ,  B60W60/0011 ,  G06F18/2155 ,  G06V10/763 ,  G06V20/56

Abstract: In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.

公开(公告)号：US20240217557A1

公开(公告)日：2024-07-04

申请号：US18602802

申请日：2024-03-12

Applicant: NVIDIA Corporation

Inventor： Fangkai Yang ,  David Nister ,  Yizhou Wang ,  Rotem Aviv ,  Julia Ng ,  Birgit Henke ,  Hon Leung Lee ,  Yunfei Shi

IPC: B60W60/00 ,  B60W30/18 ,  G08G1/0967

CPC classification number: B60W60/0027 ,  B60W30/18154 ,  B60W30/18159 ,  G08G1/096725 ,  B60W2420/403 ,  B60W2420/408 ,  B60W2552/05

Abstract: In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.

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

IPC: G06N3/088 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/088 ,  G06N3/044 ,  G06N3/045

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.