Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive sensor data in a time series from a sensor, identify an object in the sensor data, generate anonymized data for the object at a first time in the time series based on the sensor data of the object at the first time, and apply the same anonymized data to an instance of the object in the sensor data at a second time in the time series. The object includes personally identifiable information.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive first speech data, remove a first vector of speaker-identifying characteristics from the first speech data to generate extracted first speech data, generate a random vector of the speaker-identifying characteristics, and generate second speech data by applying the random vector to the extracted first speech data.

Abstract: A vehicle control system includes a camera configured to capture image data depicting a field of view proximate the vehicle is disclosed. The vehicle control system further includes a plurality of light sources in connection with the vehicle and a controller. The controller is configured to activate a plurality of lights in an alternating pattern and capture light reflected from at least one object with the camera at a time and corresponding to the alternating pattern of the plurality of lights. In response to variations in the light impinging upon the at least one object from the alternating pattern, the controller is configured to identify a distance of the object.

Abstract: A computer that includes a processor and a memory, the memory including instructions executable by the processor can activate a plurality of light sources in an alternating pattern at an illumination frequency and acquire image data depicting light reflected from an object at a timing corresponding to the alternating pattern at the illumination frequency. In response to variations in the light impinging upon the object from the alternating pattern, an object contour can be determined based on estimating one or more object surface normals based on photometric stereo. One or more of object identity and object depth can be determined based on combining the object contour with the image data.

Abstract: The disclosure relates to augmented reality vehicle identification with visual light communication. For example, a mobile device may be configured for “scanning” an area having multiple parked vehicles within visual range of the mobile device, to identify a target vehicle. The mobile device may include an application for identifying the target vehicle using visual light communication (VLC) equipment and techniques that present an augmented reality outline or other identification of the target vehicle on the smartphone screen once the vehicle is identified by the system. The encrypted communication channels with the vehicle may be established to utilize vehicle headlamps, interior lights, or another light emitting device to establish the VLC between the user's phone and the vehicle VLC system.

Abstract: An image is received from a camera built into a cabin of a vehicle. The image is demosaiced and its noise is reduced. A segmentation algorithm is applied to the image. A global illumination for the image is solved. Based on the segmentation of the image and the global illumination, a bidirectional reflectance distribution function (BRDF) for color and/or reflectance information of material in the cabin area of the vehicle is solved for. A white balance matrix and a color correction matrix for the image are computed based on the BRDF. The white balance matrix and the color correction matrix are applied to the image, which is then displayed or stored for addition image processing.

Abstract: A system includes a processor configured to request capture of image data of an environment surrounding the user, responsive to a margin of error of a detected location of a user being above a predefined threshold. The processor is also configured to process the image data to determine an actual user location relative to a plurality of objects, having known positions, identifiable in the image data and replace the detected location with the determined actual user location.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to collect first sensor data from a time-of-flight sensor, collect second sensor data from one or more sensors, generate a virtual map of at least one of a light reflectivity or a depth from the time-of-flight sensor from the collected first sensor data and the collected second sensor data, determine a difference between the light reflectivity or the depth of each pixel of the first sensor data from the light reflectivity or the depth of each corresponding pixel of the virtual map, determine an occlusion of the time-of-flight sensor as a number of pixels having respective differences of the light reflectivity or the depth exceeding an occlusion threshold, and actuate a component to clean the time-of-flight sensor when the occlusion exceeds an occlusion threshold.

Abstract: A system includes a sensor including a window; at least three lamps positioned to illuminate the window and be able to generate different ranges of wavelengths than one another; a cleaning component positioned to clean the window; and a computer communicatively coupled to the sensor, the lamps, and the cleaning component. The computer is programmed to activate the lamps and actuate the cleaning component based on data generated by the sensor during the activations of the lamps.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive optical data from an optical sensor of a vehicle, adjust the optical data using an adjustment model, measure a first value from the optical data over a duration, predict a second value from the optical data over the duration based on the first value, measure the second value from the optical data over the duration, and modify the adjustment model using the predicted second value and the measured second value. The first value and the second value are time-varying and aggregated from the optical data per time step.

Abstract: A system, comprising a processor and a memory. The memory stores instructions executable by the processor to determine, from an image including a portion of a surface of a human body, a reflected light intensity from the body surface portion, determine, a skin reflectance of the body surface portion based on a location of the body surface, a light source, and an image sensor location, and to determine, for the body surface portion, an incoming radiance, based on the skin reflectance and the reflected light intensity.

Abstract: A system for detecting a road surface includes a processor programmed to identify, in a vehicle sensor field of view, environment features including a sky, a road surface, a road shoulder, based on map data and data received from the one or more vehicle sensors, upon determining a low confidence in identifying an area of the field of view to be a road surface or sky, to receive a polarimetric image, and to determine, based on the received polarimetric image, whether the identified area is a mirage phenomenon, a wet road surface, or the sky. The low confidence is determined upon determining that a road surface has a vanishing edge, a road lane marker is missing, or an anomalous object is present.

Abstract: A system includes a processor configured to receive imaging of an environment surrounding a user, captured by a user device. The processor is also configured to identify a plurality of non-user entities in the image. The processor is further configured to identify a user location relative to the non-user entities. The processor is also configured to generate a digital representation of the user location relative to the non-user entities and convey the digital representation to a vehicle requested by the user.

Abstract: A computer includes a processor and a memory. The memory stores instructions executable by the processor such that the computer is programmed to input to a trained machine learning program, (1) a sensor fusion error that measures a statistical correlation of data received from a radar sensor and a second sensor in a vehicle, (2) a radar detection range, (3) an amount of reflection from a radar radome, (4) weather data, and (5) aerodynamic data that measures an aerodynamic drag opposing a vehicle motion, and to output from the trained machine learning program a determination concerning a presence of the radar radome.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to obtain velocity lidar point cloud data acquired with a frequency modulated continuous wave (FMCW) lidar sensor, wherein the velocity lidar point cloud data includes a speed with which a data point is moving with respect to the FMCW lidar sensor, filter the velocity lidar point cloud data to select static velocity data points, wherein the static velocity data points are velocity data points each correspond to a point on a roadway around a vehicle. The instructions can include further instructions to determine FMCW lidar sensor accelerations in six degrees of freedom based on the static velocity lidar data points and determine FMCW lidar sensor rotations and translations in six degrees of freedom based on the FMCW lidar sensor accelerations in six degrees of freedom.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive optical data from an optical sensor of a vehicle, adjust the optical data using an adjustment model, measure a first value from the optical data over a duration, predict a second value from the optical data over the duration based on the first value, measure the second value from the optical data over the duration, and modify the adjustment model using the predicted second value and the measured second value. The first value and the second value are time-varying and aggregated from the optical data per time step.

Abstract: Devices, systems, and methods for generating illumination-invariant images are disclosed. A method may include activating, by a device, a camera to capture first image data; while the camera is capturing the first image data, activating of a first, light source; receiving the first image data, the first image data having pixels having first color values; identifying first light generated by the first light source while the camera is capturing the first image data; identifying, based on the first image data, second light generated by a second light source; generating, based on the first light and the second light, second image data that are illumination-invariant; and presenting the second image data.

Abstract: An image is received from a camera built into a cabin of a vehicle. The image is demosaiced and its noise is reduced. A segmentation algorithm is applied to the image. A global illumination for the image is solved. Based on the segmentation of the image and the global illumination, a bidirectional reflectance distribution function (BRDF) for color and/or reflectance information of material in the cabin area of the vehicle is solved for. A white balance matrix and a color correction matrix for the image are computed based on the BRDF. The white balance matrix and the color correction matrix are applied to the image, which is then displayed or stored for addition image processing.

Abstract: A method includes predicting an environmental condition at a location to which a vehicle is travelling, the environmental condition including at least one of water, dust, and pollution, determining that an object within the vehicle is at a distance greater than a threshold distance from an unobstructed window of the vehicle, and then actuating the unobstructed window to a closed position based on the environmental condition and the object being at the distance from the window greater than the threshold distance.

Abstract: A system includes a computer having a processor and a memory storing instructions executable by the processor to determine a first temperature of a fluid stored by a fluid storage device and then actuate a fluid heating device to add heat energy to the fluid. The instructions include instructions to determine an amount of the heat energy added to the fluid. The instructions include instructions to determine a second temperature of the fluid stored by the fluid storage device after adding the heat energy to the fluid. The instructions include instructions to determine a quantity of the fluid stored by the fluid storage device based on the amount of the heat energy added to the fluid by the fluid heating device and a difference between the first temperature and the second temperature.