Nvidia Corporation

Igor Tryndin

David Nister

Minwoo Park

Neda Cvijetic

Junghyun Kwon

Berta Rodriguez Hervas

Trung Pham

Hang Dou

Sayed Mehdi Sajjadi Mohammadabadi

Yu Wang

Hae-Jong Seo

Abhishek Bajpayee

In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.

Intersection detection and classification in autonomous machine 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, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs&#x2014;such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.

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Fremont, CA, United States of America

Igor Tryndin