Nvidia Corporation

Abhishek Bajpayee

Hae-Jong Seo

Minwoo Park

Yu Wang

Yilin Yang

Xiaolin Lin

Eric Jonathan Yuan

Xudong Chen

David Nister

Neda Cvijetic

Igor Tryndin

Dylan Doblar

Arjun Gupta

Keerthi Raj Nagaraja

Sai Krishnan Chandrasekar

Kivanc Temel

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.

Landmark detection using curve fitting for autonomous driving applications

The present disclosure relates to determining a first illumination level corresponding to an area based at least on a first illumination detection obtained using a first illumination detector corresponding to a machine. A second illumination level corresponding to the area may be determined based at least on a second illumination detection obtained using a second illumination detector corresponding to the machine. Based at least on the first illumination level and the second illumination level, a scene illumination state of the area may be determined. Based at least on the scene illumination state, one or more lights of the machine may be controlled.

Scene illumination detection for autonomous systems and applications

In various examples, contrast values corresponding to pixels of one or more images generated using one or more sensors of a vehicle may be computed to detect and identify objects that trigger glare mitigating operations. Pixel luminance values are determined and used to compute a contrast value based on comparing the pixel luminance values to a reference luminance value that is based on a set of the pixels and the corresponding luminance values. A contrast threshold may be applied to the computed contrast values to identify glare in the image data to trigger glare mitigating operations so that the vehicle may modify the configuration of one or more illumination sources so as to reduce glare experienced by occupants and/or sensors of the vehicle.

Glare mitigation using image contrast analysis for autonomous systems and applications

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation&#x2014;e.g., Bezier curve fitting&#x2014;to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques&#x2014;such as clustering and temporal smoothing&#x2014;may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks&#x2014;e.g., lane line, road boundary line, crosswalk, pole, text, etc.&#x2014;may be used by a vehicle to perform one or more operations for navigating an environment.

In various examples, a deep neural network (DNN) is trained for sensor blindness detection using a region and context-based approach. Using sensor data, the DNN may compute locations of blindness or compromised visibility regions as well as associated blindness classifications and/or blindness attributes associated therewith. In addition, the DNN may predict a usability of each instance of the sensor data for performing one or more operations&#x2014;such as operations associated with semi-autonomous or autonomous driving. The combination of the outputs of the DNN may be used to filter out instances of the sensor data&#x2014;or to filter out portions of instances of the sensor data determined to be compromised&#x2014;that may lead to inaccurate or ineffective results for the one or more operations of the system.

Deep neural network processing for sensor blindness detection in autonomous machine applications

Growing community of inventors

San Jose, CA, United States of America

Abhishek Bajpayee