公开(公告)号：US20240096102A1

公开(公告)日：2024-03-21

申请号：US18366298

申请日：2023-08-07

Applicant: NVIDIA Corporation

Inventor： Alexander POPOV ,  David NISTER ,  Nikolai SMOLYANSKIY ,  PATRIK GEBHARDT ,  Ke CHEN ,  Ryan OLDJA ,  Hee Seok LEE ,  Shane MURRAY ,  Ruchi BHARGAVA ,  Tilman WEKEL ,  Sangmin OH

IPC: G06V20/56 ,  G01S13/89 ,  G01S17/89 ,  G06V10/774

CPC classification number: G06V20/56 ,  G01S13/89 ,  G01S17/89 ,  G06V10/774

Abstract: Systems and methods are disclosed that relate to freespace detection using machine learning models. First data that may include object labels may be obtained from a first sensor and freespace may be identified using the first data and the object labels. The first data may be annotated to include freespace labels that correspond to freespace within an operational environment. Freespace annotated data may be generated by combining the one or more freespace labels with second data obtained from a second sensor, with the freespace annotated data corresponding to a viewable area in the operational environment. The viewable area may be determined by tracing one or more rays from the second sensor within the field of view of the second sensor relative to the first data. The freespace annotated data may be input into a machine learning model to train the machine learning model to detect freespace using the second data.

公开(公告)号：US20250110213A1

公开(公告)日：2025-04-03

申请号：US18477325

申请日：2023-09-28

Applicant: NVIDIA Corporation

Inventor： Patrik GEBHARDT ,  Alexander POPOV ,  Shane MURRAY

IPC: G01S7/41 ,  G01S13/58 ,  G01S13/931

Abstract: Embodiments of the present disclosure relate to a system and method used to transfer image data via Ethernet. In some embodiments, the method may include determining, using a machine learning model, an estimated velocity corresponding to an object based at least on measured RADAR data, where the measured RADAR data may correspond to RADAR detections associated with the object. In some embodiments, the method may further include determining expected RADAR data corresponding to the object based at least on the estimated velocity. Some embodiments may additionally include updating one or more parameters of the machine learning model based on the difference between the measured RADAR data and the expected RADAR data.

公开(公告)号：US20240411008A1

公开(公告)日：2024-12-12

申请号：US18331402

申请日：2023-06-08

Applicant: NVIDIA Corporation

Inventor： James CRITCHLEY ,  Kyle KOLASINSKI ,  Shane MURRAY

IPC: G01S13/72 ,  G01S13/42 ,  G01S13/931

Abstract: One or more embodiments of the present disclosure relate to obtaining a first state estimate corresponding to an object, the first state estimate including a first velocity vector estimate corresponding to the object. The disclosure may further relate to receiving first sensor data corresponding to a first portion of the object. The embodiments may further include determining a first expected measurement corresponding to the first portion, the first expected measurement including a first expected range rate determined based at least on the first angle measurement and the first velocity vector estimate of the first state estimate. And, determining a second state estimate corresponding to the object, the second state estimate including a second velocity vector estimate corresponding to the object and determined based at least on the first range rate measurement and the first expected range rate.