公开(公告)号：US20240017745A1

公开(公告)日：2024-01-18

申请号：US17865344

申请日：2022-07-14

Applicant: NVIDIA Corporation

Inventor： Yulong Cao ,  Chaowei Xiao ,  Danfei Xu ,  Anima Anandkumar ,  Marco Pavone

IPC: B60W60/00 ,  B60W40/04 ,  B60W50/00

CPC classification number: B60W60/0027 ,  B60W40/04 ,  B60W50/0097 ,  B60W2554/4041 ,  B60W2554/4044

Abstract: Apparatuses, systems, and techniques to generate trajectory data for moving objects. In at least one embodiment, adversarial trajectories are generated to evaluate a trajectory prediction model and are based, at least in part, on a differentiable dynamic model.

公开(公告)号：US20240095077A1

公开(公告)日：2024-03-21

申请号：US18122594

申请日：2023-03-16

Applicant: NVIDIA Corporation

Inventor： Ishika Singh ,  Arsalan Mousavian ,  Ankit Goyal ,  Danfei Xu ,  Jonathan Tremblay ,  Dieter Fox ,  Animesh Garg ,  Valts Blukis

IPC: G06F9/50 ,  G06N20/00

CPC classification number: G06F9/5027 ,  G06N20/00

Abstract: Apparatuses, systems, and techniques to generate a prompt for one or more machine learning processes. In at least one embodiment, the machine learning process(es) generate(s) a plan to perform a task (identified in the prompt) that is to be performed by an agent (real world or virtual).

公开(公告)号：US20240028673A1

公开(公告)日：2024-01-25

申请号：US18180476

申请日：2023-03-08

Applicant: NVIDIA Corporation

Inventor： Chaowei Xiao ,  Yolong Cao ,  Danfei Xu ,  Animashree Anandkumar ,  Marco Pavone ,  Xinshuo Weng

IPC: G06F21/14 ,  B60W60/00

CPC classification number: G06F21/14 ,  B60W60/0011

Abstract: In various examples, robust trajectory predictions against adversarial attacks in autonomous machines and applications are described herein. Systems and methods are disclosed that perform adversarial training for trajectory predictions determined using a neural network(s). In order to improve the training, the systems and methods may devise a deterministic attach that creates a deterministic gradient path within a probabilistic model to generate adversarial samples for training. Additionally, the systems and methods may introduce a hybrid objective that interleaves the adversarial training and learning from clean data to anchor the output from the neural network(s) on stable, clean data distribution. Furthermore, the systems and methods may use a domain-specific data augmentation technique that generates diverse, realistic, and dynamically-feasible samples for additional training of the neural network(s).

公开(公告)号：US20230391365A1

公开(公告)日：2023-12-07

申请号：US18114035

申请日：2023-02-24

Applicant: NVIDIA Corporation

Inventor： Boris Ivanovic ,  Danfei Xu ,  Yuxiao Chen ,  Marco Pavone

IPC: B60W60/00 ,  G06N3/045

CPC classification number: B60W60/0011 ,  B60W60/00274 ,  G06N3/045 ,  B60W2554/4041 ,  B60W2554/4044 ,  B60W2554/80 ,  B60W2556/40

Abstract: In various examples, techniques for generating simulations for autonomous machines and applications are described herein. Systems and methods are disclosed that use various models to generate simulations. For instance, a first model(s) may process input data, such as input data representing maps indicating the locations of objects and state history of the objects within the environment, to determine navigation goals for the objects. Additionally, a second model(s) may then process the input data and data representing the navigation goals in order to determine possible trajectories (e.g., action samples) for the objects within the environment. Furthermore, a third model(s) may process the input data to predict trajectories of the objects within the environment. The systems and methods may then use at least the possible trajectories and the predicted trajectories to simulate the motion (e.g., one or more trajectories) of one or more of the objects.