Apple Inc.

Argo Ai Gmbh

Ford Global Technolgoies, LLC

Koji Gardiner

Jason Rundle Rukes

E-Cheng Chang

Christopher Nelson St John

Michael V Morelli

Morgan M Wagner

Bowei Zhang

Dikpal Reddy

Duc H Vu

Eric K Thong

Laurens M Schouten-Evers

Ravi Basavaraj

Hsin Miao

Yu-Wei Chang

Erli Ding

Alan William Yip

E-Chang Chang

Devices, systems, and methods are provided for sensor health and regression testing. A sensor testing system may include a first plurality of sensor testing targets positioned on a first side of a vehicle at a first distance from the vehicle, a second plurality of sensor testing targets positioned on a second side of the vehicle at the first distance, and a first sensor testing target positioned at a second distance from the vehicle, the second distance further from the vehicle than the first distance. The first and second pluralities of sensor testing targets both may include three or more sensor testing targets for testing cameras and light detection and ranging (LIDAR) sensors of the vehicle. The first sensor testing target may be used to test at least one of camera or LIDAR data. The sensor testing system may identify degradation of sensor performance, triggering further analysis and/or repairs.

Enhanced sensor health and regression testing for vehicles

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

Systems, methods, and computer-readable media are disclosed for identifying light flares in images. An example method may involve receiving an image from an imaging device, the image including data indicative of a flare artifact originating from a region of the image. The example method may also involve determining, based on the image data, a first array of pixels extending radially outwards from the region and a second array of pixels extending radially outwards from the region. The example method may also involve creating, based on the image data, a flare array, the flare array including the first array of pixels and the second array of pixels. The example method may also involve determining, based on the flare array, a peak flare artifact value indicative of a size of the flare artifact. The example method may also involve determining, based on the peak flare artifact value, a flare artifact score for the imaging device.

Systems and methods for quantifying light flares in images

A method for calibration of vision sensors includes, by a processor: selecting a calibration sequence that has a base calibration pattern and calibration angles, generating a calibrating target that may include the calibration pattern at a selected calibration angle of the angles, and causing a display screen to display a digital image representative of the target at the selected angle relative to an originating border of the selected angle. Each of the calibration angles is associated with a different originating border of the screen. The method includes by a vision sensor capturing at least one image of the calibrating target displayed on the screen. The calibration is repeated for each calibration angle of the sequence. The method includes performing calibration of the vision sensor in response to image signal processing of extracted calibration features in the at least one image of the calibrating targets.

Method and system to calibrate camera devices of a vehicle vision system using a programmable calibration target device

A portable electronic device includes a camera and a display switchable between a portrait mode and a landscape mode in response to an inertial sensor. When it is determined that the display mode should be switched in response to movement of the device, a feature of a user of the device, such as the user's face, is imaged with the camera. If the imaged feature changes orientation with respect to the display by less than a threshold amount, then the switch in display mode is disabled and the display remains in its current mode. Other embodiments are also described and claimed.

Camera-based orientation fix from portrait to landscape

An automated image capture mode of a camera in an electronic device insures that a particular subject appears in the captured image. An image of the particular subject, which may be the photographer, is initially captured. Subsequently, another image capture is automatically triggered when the same subject is detected within the camera's field of view. In one embodiment, a motion sensor within the device may be employed to begin a search for the subject, when the camera is subjected to a sudden movement. Other embodiments are also described and claimed.

Auto-triggered camera self-timer based on recognition of subject's presence in scene

Growing community of inventors

San Francisco, CA, United States of America

Koji Gardiner