Ford Global Technolgoies, LLC

Intel Corporation

Argo Ai Gmbh

Ravi Basavaraj

Richard L Kwant

Neetu Agarwal

Rastislav Lukac

Gazi Naser Ali

Christopher Nelson St John

Bowei Zhang

Koji Gardiner

Krishna Kumar Madaparambil

Systems and methods are provided for operating a vehicle, is provided. The method includes, by a vehicle control system of the vehicle, identifying map data for a present location of the vehicle using a location of the vehicle and pose and trajectory data for the vehicle, identifying a field of view of a camera of the vehicle, and analyzing the map data to identify an object that is expected to be in the field of view of the camera. The method further includes, based on (a) a class of the object, (b) characteristics of a region of interest in the field of view of the vehicle, or (c) both, selecting an automatic exposure (AE) setting for the camera. The method additionally includes causing the camera to use the AE setting when capturing images of the object, and using the camera, capturing the images of the object.

Estimating auto exposure values of camera by prioritizing object of interest based on contextual inputs from 3D maps

Systems, methods, and computer-readable media are disclosed for improved camera color calibration. An example method may involve capturing a first wavelength emitted by a first type of traffic light. The example method may also involve determining, based on the first wavelength, a first color value associated with the wavelength emitted by the first type of traffic light. The example method may also involve capturing, by a first camera, a first image, video, or real-time feed of a first portion of a test target, the first portion of the test target including a first light color that is based on the first color value. The example method may also involve determining, based on the first image, video, or real-time feed of the first portion of a test target, a second color value output by the camera. The example method may also involve determining, based on a comparison between the first color value and the second color value, that a difference exists between the first color value and the second color value. The example method may also involve adjusting, based on the detection that a difference exists between the first color value and the second color value, at least one of: a property of the camera or signal processing associated with the camera.

Systems and methods for improved camera color calibration

An apparatus comprising a memory to store a first image captured by a first camera and a second image captured by a second camera; and a processor comprising circuitry, the processor to identify viewpoint information defining a view for a stitched image, the stitched image to be generated from a combination of a plurality of images comprising the first image and the second image; and based on the viewpoint information and a projection type, determine transformation parameters for at least one reference region associated with overlapping regions of the first image and the second image in order to: localize overlapping regions of the first image and the second image to determine stitching parameters; combine the first image and the second image using the stitching parameters; and assign data of the combined first image and the second image to the stitched image.

Surround camera system with seamless stitching for arbitrary viewpoint selection

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Sunnyvale, CA, United States of America

Ravi Basavaraj