公开(公告)号：US20240391495A1

公开(公告)日：2024-11-28

申请号：US18674591

申请日：2024-05-24

Applicant: NVIDIA Corporation

Inventor： David Nister

IPC: B60W60/00

Abstract: Path planning may be performed using a configuration space parameterized, at least in part, by one or more variables corresponding to curvature for an object and/or velocity of the objects. The configuration space may be discretized using for maneuver types and/or maneuvers using 4D or 5D volumes or arrays of 3D volumes. A maneuver type and/or maneuver may correspond to a constant rate of change of curvature for the object. Corresponding maneuver types may then form respective clothoid maneuvers to model smooth or gradual changes to steering and curvature. The maneuvers and/or maneuver types that have varying curvature may span across an array of fixed curvature volumes (e.g., 3D volumes) as opposed to being confined to a single volume. A transition volume(s) may be used to represent the object being stopped while changing gear.

公开(公告)号：US12051206B2

公开(公告)日：2024-07-30

申请号：US16938706

申请日：2020-07-24

Applicant: NVIDIA Corporation

Inventor： Ke Chen ,  Nikolai Smolyanskiy ,  Alexey Kamenev ,  Ryan Oldja ,  Tilman Wekel ,  David Nister ,  Joachim Pehserl ,  Ibrahim Eden ,  Sangmin Oh ,  Ruchi Bhargava

IPC: G06T7/00 ,  G05D1/00 ,  G06F18/00 ,  G06F18/22 ,  G06F18/23 ,  G06T5/50 ,  G06T7/10 ,  G06T7/11 ,  G06V10/82 ,  G06V20/56 ,  G06V20/58 ,  G06V10/44

CPC classification number: G06T7/11 ,  G05D1/0088 ,  G06F18/22 ,  G06F18/23 ,  G06T5/50 ,  G06T7/10 ,  G06V10/82 ,  G06V20/56 ,  G06V20/58 ,  G06T2207/10028 ,  G06T2207/20084 ,  G06T2207/30252 ,  G06V10/454

Abstract: A deep neural network(s) (DNN) may be used to perform panoptic segmentation by performing pixel-level class and instance segmentation of a scene using a single pass of the DNN. Generally, one or more images and/or other sensor data may be stitched together, stacked, and/or combined, and fed into a DNN that includes a common trunk and several heads that predict different outputs. The DNN may include a class confidence head that predicts a confidence map representing pixels that belong to particular classes, an instance regression head that predicts object instance data for detected objects, an instance clustering head that predicts a confidence map of pixels that belong to particular instances, and/or a depth head that predicts range values. These outputs may be decoded to identify bounding shapes, class labels, instance labels, and/or range values for detected objects, and used to enable safe path planning and control of an autonomous vehicle.

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

IPC: B60W60/00 ,  B60W50/00 ,  G06N3/08

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.

公开(公告)号：US20240230339A1

公开(公告)日：2024-07-11

申请号：US18615894

申请日：2024-03-25

Applicant: NVIDIA Corporation

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

IPC: G01C21/26 ,  G06N3/04 ,  G06N3/08 ,  G06V10/44 ,  G06V10/46 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56

CPC classification number: G01C21/26 ,  G06N3/04 ,  G06N3/08 ,  G06V10/454 ,  G06V10/462 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56 ,  G06F2218/12

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to generate potential paths for the vehicle to navigate an intersection in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as heat maps corresponding to key points associated with the intersection, vector fields corresponding to directionality, heading, and offsets with respect to lanes, intensity maps corresponding to widths of lanes, and/or classifications corresponding to line segments of the intersection. The outputs may be decoded and/or otherwise post-processed to reconstruct an intersection—or key points corresponding thereto—and to determine proposed or potential paths for navigating the vehicle through the intersection.

公开(公告)号：US12013244B2

公开(公告)日：2024-06-18

申请号：US16848102

申请日：2020-04-14

Applicant: NVIDIA Corporation

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

IPC: G05D1/00 ,  G01C21/26 ,  G06N3/04 ,  G06N3/08 ,  G06V10/44 ,  G06V10/46 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56 ,  B60W30/18 ,  B60W60/00 ,  G06F18/2413 ,  G06N3/02 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/048 ,  G06N3/088 ,  G06N5/01 ,  G06N7/01 ,  G06N20/00 ,  G06N20/10 ,  G08G1/16

CPC classification number: G01C21/26 ,  G05D1/0083 ,  G05D1/0246 ,  G06N3/04 ,  G06N3/08 ,  G06V10/454 ,  G06V10/462 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56 ,  G06F2218/12

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to generate potential paths for the vehicle to navigate an intersection in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as heat maps corresponding to key points associated with the intersection, vector fields corresponding to directionality, heading, and offsets with respect to lanes, intensity maps corresponding to widths of lanes, and/or classifications corresponding to line segments of the intersection. The outputs may be decoded and/or otherwise post-processed to reconstruct an intersection—or key points corresponding thereto—and to determine proposed or potential paths for navigating the vehicle through the intersection.

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：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

IPC: B60W60/00 ,  B60W50/00 ,  G05D1/00 ,  G06N3/08

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.

公开(公告)号：US11941819B2

公开(公告)日：2024-03-26

申请号：US17457825

申请日：2021-12-06

Applicant: NVIDIA Corporation

Inventor： Dongwoo Lee ,  Junghyun Kwon ,  Sangmin Oh ,  Wenchao Zheng ,  Hae-Jong Seo ,  David Nister ,  Berta Rodriguez Hervas

IPC: G06T7/13 ,  G06T7/40 ,  G06T17/30 ,  G06V10/44 ,  G06V10/75 ,  G06V10/772 ,  G06V10/82 ,  G06V20/58

CPC classification number: G06T7/13 ,  G06T7/40 ,  G06T17/30 ,  G06V10/454 ,  G06V10/751 ,  G06V10/772 ,  G06V10/82 ,  G06V20/586 ,  G06T2207/10021 ,  G06T2207/20084 ,  G06T2207/30264

Abstract: A neural network may be used to determine corner points of a skewed polygon (e.g., as displacement values to anchor box corner points) that accurately delineate a region in an image that defines a parking space. Further, the neural network may output confidence values predicting likelihoods that corner points of an anchor box correspond to an entrance to the parking spot. The confidence values may be used to select a subset of the corner points of the anchor box and/or skewed polygon in order to define the entrance to the parking spot. A minimum aggregate distance between corner points of a skewed polygon predicted using the CNN(s) and ground truth corner points of a parking spot may be used simplify a determination as to whether an anchor box should be used as a positive sample for training.

公开(公告)号：US20240062657A1

公开(公告)日：2024-02-22

申请号：US18491492

申请日：2023-10-20

Applicant: NVIDIA Corporation

Inventor： Yue Wu ,  Pekka Janis ,  Xin Tong ,  Cheng-Chieh Yang ,  Minwoo Park ,  David Nister

IPC: G08G1/16 ,  G06V10/82 ,  G06V20/58 ,  G06V20/10 ,  G06F18/214 ,  G05D1/00 ,  G05D1/02 ,  G06N3/04 ,  G06T7/20

CPC classification number: G08G1/166 ,  G06V10/82 ,  G06V20/58 ,  G06V20/10 ,  G06F18/214 ,  G05D1/0088 ,  G05D1/0289 ,  G06N3/0418 ,  G06T7/20 ,  G05D2201/0213

Abstract: In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.

公开(公告)号：US11908203B2

公开(公告)日：2024-02-20

申请号：US17718721

申请日：2022-04-12

Applicant: Nvidia Corporation

Inventor： Ishwar Kulkarni ,  Ibrahim Eden ,  Michael Kroepfl ,  David Nister

IPC: G06V20/58 ,  G06T15/04 ,  G01S17/89 ,  G06T11/00 ,  G06T7/521 ,  G06T7/30 ,  G06V20/56

CPC classification number: G06V20/58 ,  G01S17/89 ,  G06T7/30 ,  G06T7/521 ,  G06T11/001 ,  G06T15/04 ,  G06V20/56 ,  G06V20/582 ,  G06V20/584 ,  G06T2207/10028 ,  G06T2207/30241

Abstract: LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. Improved techniques for processing the point cloud data that has been collected are provided. The improved techniques include mapping one or more point cloud data points into a depth map, the one or more point cloud data points being generated using one or more sensors; determining one or more mapped point cloud data points within a bounded area of the depth map, and detecting, using one or more processing units and for an environment surrounding a machine corresponding to the one or more sensors, a location of one or more entities based on the one or more mapped point cloud data points.