公开(公告)号：US20240161341A1

公开(公告)日：2024-05-16

申请号：US18166118

申请日：2023-02-08

Applicant: NVIDIA Corporation

Inventor： Ayon Sen ,  Gang Pan ,  Cheng-Chieh Yang ,  Yue Wu

IPC: G06T7/80

CPC classification number: G06T7/80 ,  G06T2207/10028 ,  G06T2207/20084

Abstract: In various examples, sensor configuration for autonomous or semi-autonomous systems and applications is described. Systems and methods are disclosed that may use image feature correspondences between camera images along with an assumption that image features are locally planar to determine parameters for calibrating an image sensor with a LiDAR sensor and/or another image sensor. In some examples, an optimization problem is constructed that attempts to minimize a geometric loss function, where the geometric loss function encodes the notion that corresponding image features are views of a same point on a locally planar surface (e.g., a surfel or mesh) that is constructed from LiDAR data generated using a LiDAR sensor. In some examples, performing such processes to determine the calibration parameters may remove structure estimation from the optimization problem.

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：US20240059295A1

公开(公告)日：2024-02-22

申请号：US18498370

申请日：2023-10-31

Applicant: NVIDIA Corporation

Inventor： Minwoo Park ,  Yue Wu ,  Cheng-Chieh Yang

IPC: B60W40/02 ,  G06T7/30 ,  G06T7/80 ,  H04N13/296 ,  H04N13/271 ,  G06T15/10 ,  G06V20/58 ,  B60W60/00 ,  G06T7/593

CPC classification number: B60W40/02 ,  G06T7/30 ,  G06T7/85 ,  H04N13/296 ,  H04N13/271 ,  G06T15/10 ,  G06V20/58 ,  B60W60/001 ,  G06T7/593 ,  B60W2420/42 ,  G06T2207/10012 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20132 ,  G06T2207/20228 ,  G06T2207/30261 ,  G06T2207/30244

Abstract: In various examples, systems and methods are disclosed that detect hazards on a roadway by identifying discontinuities between pixels on a depth map. For example, two synchronized stereo cameras mounted on an ego-machine may generate images that may be used extract depth or disparity information. Because a hazard's height may cause an occlusion of the driving surface behind the hazard from a perspective of a camera(s), a discontinuity in disparity values may indicate the presence of a hazard. For example, the system may analyze pairs of pixels on the depth map and, when the system determines that a disparity between a pair of pixels satisfies a disparity threshold, the system may identify the pixel nearest the ego-machine as a hazard pixel.

公开(公告)号：US11657532B2

公开(公告)日：2023-05-23

申请号：US17103680

申请日：2020-11-24

Applicant: NVIDIA Corporation

Inventor： Minwoo Park ,  Yue Wu ,  Michael Grabner ,  Cheng-Chieh Yang

IPC: G06T7/60 ,  G06T7/579 ,  G06V20/56

CPC classification number: G06T7/60 ,  G06T7/579 ,  G06V20/588 ,  G06T2200/08 ,  G06T2207/10028 ,  G06T2207/30256

Abstract: In various examples, surface profile estimation and bump detection may be performed based on a three-dimensional (3D) point cloud. The 3D point cloud may be filtered in view of a portion of an environment including drivable free-space, and within a threshold height to factor out other objects or obstacles other than a driving surface and protuberances thereon. The 3D point cloud may be analyzed—e.g., using a sliding window of bounding shapes along a longitudinal or other heading direction—to determine one-dimensional (1D) signal profiles corresponding to heights along the driving surface. The profile itself may be used by a vehicle—e.g., an autonomous or semi-autonomous vehicle—to help in navigating the environment, and/or the profile may be used to detect bumps, humps, and/or other protuberances along the driving surface, in addition to a location, orientation, and geometry thereof.