Abstract: A vehicle system includes a display unit and a computer communicatively coupled to the camera. The display unit includes a display panel, a circuit board fixed relative to the display panel, a plurality of visible-light light emitting diodes (LEDs) mounted to the circuit board, and a plurality of infrared LEDs mounted to the circuit board and interspersed with the visible-light LEDs. The circuit board is arranged to direct emissions from the visible-light LEDs and infrared LEDs to the display panel. The computer is programmed to actuate a component based on an infrared image from a camera depicting an occupant illuminated by the infrared LEDs, and adjust a brightness of the infrared LEDs based on an ambient brightness.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire one or more images from a camera and determine first camera noise values based on the one or more images by determining reactions of camera photo receptors to light. The instructions can include further instructions to compare the first camera noise values with second camera noise values determined based on previously acquired images from the camera and output a tamper determination for the camera based on whether the first camera noise values match, within a tolerance value, the second camera noise values determined based on the previously acquired images from the camera.

Abstract: In a vehicle, an onboard vehicle camera may identify, based on non-camera sensor data, a first portion of an image obtained by the camera that includes an area of interest. A first compression rule may be applied to portions of the image that include the area of interest. A second compression rule may be applied to portions of the image that exclude the area of interest.

Abstract: Systems and methods for ranking routes based on driving complexity are provided. The systems and methods may generate routes for a user based on factors indicative of difficulty levels of the routes (for example, based on the types of road features that exist on the route, among various other factors). The difficulty levels of the generated routes may then be used to automatically select, by a navigation system of a vehicle, a route that is suitable for the user based on their driving experience level. Alternatively, the routes may be presented to the user via the vehicle or a mobile device for selection by the user. The systems and methods may also be dynamic in that the difficulty levels of the routes may be adjusted as the user gains more driving experience.

Abstract: A plurality of thermal images forward of a vehicle are collected. Thermal data in the plurality of thermal images is normalized based on an ambient air temperature to generate a plurality of normalized thermal images. The plurality of normalized thermal images are input to a machine learning program trained to output an identification of an object based on the ambient air temperature and a risk of collision between the vehicle and the object. A vehicle component is actuated based on the identification of the object and the risk of collision with the object.

Abstract: A system for mitigating glare from at least one light source while driving a vehicle. The system includes determining an occurrence of a glare condition based on, at least in part, a visible light video stream generated by a visible light sensor. Upon determining the occurrence of the glare condition, indicia are presented at the vehicle display to assist in navigation of the vehicle.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image with a visible and NIR light camera and acquire a second image with an infrared camera. The instructions can include further instructions to determine whether the second image includes a live human face by comparing a first infrared profile included in the second image with second infrared profile included in a previously acquired third image acquired with the infrared camera; and when the second image includes the live human face, output the first image.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image by illuminating a first object with a first light beam, segment the first image of the first object to determine regions that correspond to a first surface material and determine a first measure of pixel values in regions of the first image that correspond to the first surface material. The instructions include further instructions to perform a comparison of the first measure of pixel values to a second measure of pixel values determined from a second image of a second object, wherein the second image is previously acquired by illuminating the second object with a second light beam and when the comparison determines that the first measure is equal to the second measure of pixel values within a tolerance, determine that the first object and the second object are a same object.

Abstract: A vehicle characterizes a sunload on the vehicle using an imaging system including at least one camera capturing image data (e.g., without using an ambient light sensor). The image data includes at least a portion of a passenger cabin. A region of interest (ROI) overlay receives the image data and extracts selected image data according to predetermined scene elements of the vehicle environment. An occupant overlay is configured to detect a vehicle occupant represented in the selected image data and configured to generate truncated image data by subtracting image data corresponding to the vehicle occupant from the selected image data. An ambient light model uses environmental parameters including a sun position to estimate an expected sunload range. A mapper generates a sunload map comprising respective sunload values for a plurality of locations on the vehicle according to the truncated image data and the expected sunload range.

Abstract: This disclosure describes systems and methods for ranking routes based on driving complexity. An example method may include receiving one or more inputs, the one or more inputs including at least a starting location, a destination location, and a user driving experience level. The example method may also include determining, based on the one or more inputs, a first difficultly value for a first route between the starting location and the destination location and a second difficulty value for a second route between the starting location and the destination location. The example method may also include determining that the first difficulty value is less than the second difficulty value. The example method may also include automatically updating, prior to a first vehicle trip, a navigation system to the first route based on the determination that the first difficulty value is less than the second difficulty value.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image from a first camera by illuminating a first object with a first light and determine an object status as one of a real object or a counterfeit object by comparing a first measure of pixel values corresponding to the first object to a threshold.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image by illuminating a first object with a first light beam, determine a first measure of pixel values in the image and perform a comparison of the first measure of pixel values to a second measure of pixel values determined from a second image of a second object, wherein the second image is previously acquired by illuminating the second object with a second light beam. The instructions include further instructions to, when the comparison determines that the first measure is equal to the second measure of pixel values within a tolerance, determine that the first object and the second object are a same object.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a first measure of pixel values in the first image, acquire a second image, estimate ambient light illuminating the first object based on the second image and modify the first measure of pixel values based on a second measure of pixel values corresponding to estimated ambient light based on the second image. The instructions include further instructions to perform a comparison of the modified first measure of pixel values to a third measure of pixel values determined from a third image of a second object, wherein the third image is previously acquired by illuminating the second object with a second light beam and, when the comparison determines that the first measure is equal to the third measure of pixel values within a tolerance, determine whether the first object and the second object are a same object.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to divide each of one or more images acquired by a camera into a plurality of zones, determine respective camera noise values for respective zones based on the one or more images, determine one or more zone expected values for the one or more images by summing camera noise values multiplied by scalar coefficients for each zone and normalizing the sum by dividing by a number of zones in the plurality of zones, and determine a source of the camera as being one of the same camera or an unknown camera based on comparing the one or more zone expected values to previously acquired expected zone values.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to divide each of one or more images acquired by a camera into a plurality of zones, determine respective camera noise values for respective zones based on the one or more images, determine one or more zone expected values for the one or more images by summing camera noise values multiplied by scalar coefficients for each zone and normalizing the sum by dividing by a number of zones in the plurality of zones, and determine a source of the camera as being one of the same camera or an unknown camera based on comparing the one or more zone expected values to previously acquired expected zone values.

Abstract: First objects are detected in an infrared image from an infrared camera and second objects are detected in a color image from a color camera. The first objects are compared with the second objects to determine pairs of matching first objects and second objects. For each matching pair, a respective region of an output image is colorized by setting colors of pixels in the region based on colors of the pixels of the second object in the matching pair. The pixels in the region have locations corresponding to the locations of the pixels in the first object of the matching pair. When the colorizing is complete, pixels not in the colorized regions have intensities of the infrared image. The output image is a version of the infrared image with a region colorized according to the color image.

Abstract: A vehicle characterizes a sunload on the vehicle using an imaging system including at least one camera capturing image data (e.g., without using an ambient light sensor). The image data includes at least a portion of a passenger cabin. A region of interest (ROI) overlay receives the image data and extracts selected image data according to predetermined scene elements of the vehicle environment. An occupant overlay is configured to detect a vehicle occupant represented in the selected image data and configured to generate truncated image data by subtracting image data corresponding to the vehicle occupant from the selected image data. An ambient light model uses environmental parameters including a sun position to estimate an expected sunload range. A mapper generates a sunload map comprising respective sunload values for a plurality of locations on the vehicle according to the truncated image data and the expected sunload range.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image from a first camera by illuminating a first object with a first light and determine an object status as one of a real object or a counterfeit object by comparing a first measure of pixel values corresponding to the first object to a threshold.

Abstract: Vision sensor dynamic watermarking via noise characterization are disclosed herein. An example device can capture an image by a first device, where the image comprising a pixel group has baseline noise characterization caused by a base noise profile for the first device. The device can produce a noise watermark to create a watermarked image, where the noise watermark can be produced by altering the baseline noise characterization to produce modified noise characterization. The device can also transmit the watermarked image to a receiver.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire one or more images from a camera and determine first camera noise values based on the one or more images by determining reactions of camera photo receptors to light. The instructions can include further instructions to compare the first camera noise values with second camera noise values determined based on previously acquired images from the camera and output a tamper determination for the camera based on whether the first camera noise values match, within a tolerance value, the second camera noise values determined based on the previously acquired images from the camera.