Title:
DEEP LEARNING BASED OPERATIONAL DOMAIN VERIFICATION USING CAMERA-BASED INPUTS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
Document Type and Number:
Japanese Patent JP2023082647
Kind Code:
A
Abstract:
To provide systems and methods for deep learning based operational design domain verification using camera-based inputs for autonomous machine applications.SOLUTION: Methods and systems provided in various examples are, by using a machine learning model, to determine operational domain conditions related to an autonomous and/or semi-autonomous machine such as one or more of the following: the amount of camera blindness, the blindness classification, the illumination level, the path surface condition, the visibility distance, the scene type classification, and the distance to a scene. Once one or more of these conditions are determined, an operational level of the machine may be determined, and the machine may be controlled according to the operational level.SELECTED DRAWING: Figure 1A
Inventors:
ABHISHEK BAJPAYEE
ARJUN GUPTA
DOBLAR DYLAN
SEO HAE-JONG
GEORGE TANG
KEERTHI RAJ NAGARAJA
ARJUN GUPTA
DOBLAR DYLAN
SEO HAE-JONG
GEORGE TANG
KEERTHI RAJ NAGARAJA
Application Number:
JP2022089329A
Publication Date:
June 14, 2023
Filing Date:
June 01, 2022
Export Citation:
Assignee:
NVIDIA CORP
International Classes:
G06T7/00; G08G1/16
Attorney, Agent or Firm:
Patent Attorney Corporation Asamura Patent Office