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: The present disclosure relates to a system and a method for addressing an error in a localization system that includes monitoring a plurality of sensors of a driver assistance system in real-time, with each sensor generating a data stream. The method further includes identifying a sensor having an anomalous data stream and calculating a primary localization and a backup localization. The primary localization calculation includes the anomalous data stream and the backup localization calculation does not include the anomalous data stream. Further, the method includes executing an action when the backup localization error estimate exceeds a threshold.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to predict a user arrival time based on data of a user and a user origin location, deactivate a propulsion of the vehicle upon arriving at the user origin location, and deactivate one or more additional vehicle components based on the user arrival time.

Abstract: Systems, methods, and computer-readable media are disclosed for increasing solar power generation during vehicle parking. Example methods may include determining that a first parking spot and a second parking spot are available within a line of sight via a vehicle sensor, determining a parking duration based on a user profile and a current location of the vehicle, determining a first shading area for the first parking spot and a second shading area the second areas based on the parking duration, determining, based on the first shading area and the second shading area, potential power generation at the first parking spot and the second parking spot during the parking duration, and determining, based on the potential power generation at the first parking spot and the second parking spot during the parking duration, which parking spot to park.

Abstract: A system comprises a processor and a memory. The memory stores instructions executable by the processor to determine a baseline polarization for a surface of a material based on a first polarimetric image of the surface, to determine a second polarization for the surface based on a second polarimetric image of the surface, to determine, based on the baseline polarization data and the second polarization data, that there is a wet area on the surface, and upon determining the wet area, actuate an actuator to cause a clean the surface.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to input strain data measuring mechanical strain on a vehicle camera and operation data of a vehicle component, the operation data describing at least one of an output or a state of the vehicle component, to a machine learning program that outputs a displacement of the vehicle camera from a neutral position based on the strain data and the operation data. The instructions further include instructions to identify a transformation matrix that transforms data from the vehicle camera to a coordinate system of the vehicle camera in the neutral position based on the output displacement of the vehicle camera, to apply the transformation matrix to data collected by the vehicle camera to generate transformed data, and to actuate one of the vehicle component or a second vehicle component based on the transformed data.

Abstract: A location of a light source outside a field of view of a polarimetric image of a vehicle interior can be determined. Then, upon (a) determining, based on the polarimetric image, that the light source is other than vehicle lighting or an exterior source and (b) detecting a vehicle occupant, a vehicle actuator can be actuated based on a determined location of the light source.

Abstract: A system includes a computer including a processor programmed to identify, based on data from first sensors included in a first vehicle, that a second vehicle proximate to a route of the first vehicle is stationary. The processor is further programmed to receive, from second sensors included in the first vehicle, transient velocity data from the second vehicle; and determine an operating condition of the second vehicle based on the transient velocity data.

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 system includes a processor configured to receive image data of a scene around a user location, including identification of a plurality of vehicle objects within the image. The processor is also configured to process the image data to determine where stopping spaces, not occupied by vehicle objects, exist within the image. The processor is further configured to select a determined stopping space and provide the selected stopping space to a passenger and driver to arrange a pickup location for a ride request.

Abstract: The present disclosure discloses a system and a method for mitigating image distortion. In an example implementation, the system and the method can receive vehicle state data and vehicle inertial measurement data; generate an image distortion prediction indicative of image distortion within an image captured by the image capture assembly based on the vehicle state data and the vehicle inertial measurement data; and at least one of correct or mitigate the image distortion based on the image distortion prediction.

Abstract: The present disclosure discloses a system and a method for mitigating image distortion. In an example implementation, the system and the method can receive vehicle state data and vehicle inertial measurement data; generate an image distortion prediction indicative of image distortion within an image captured by the image capture assembly based on the vehicle state data and the vehicle inertial measurement data; and at least one of correct or mitigate the image distortion based on the image distortion prediction.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to project an icon onto a surface having a pattern, capture an image of the icon and the pattern, identify a change between the pattern in the image and a default pattern, identify a user input based on the change from the default pattern, and actuate a component based on the user input.

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: The disclosure is generally directed to systems and methods for sharing a video feed of a camera among multiple image processing components in a vehicle. A first priority may be applied to a first image processing component that performs a first image processing function. A second priority that is lower than the first priority, is applied to a second image processing component that performs a second image processing function. The first function may be deemed more important than the second function due to various reasons. Consequently, the first image processing component is offered priority to apply a first set of camera settings on the camera. The second image processing component may prefer to apply a different set of camera settings for executing the second image processing function. An access arbitrator allows the second image processing component to do so when the first image processing component relinquishes control of the camera.

Abstract: A processing system comprises a processor and a memory. The memory stores instructions executable by the processor to receive a polarimetric image from a polarimetric camera sensor, and to identify a road surface in the received image based on a vehicle location, an orientation of the camera sensor, and a vehicle pose. The memory stores instructions, upon identifying, in the polarimetric image, polarized light reflections from the identified road surface based on a polarization direction and a polarization degree determined from the polarimetric image, to remove the identified polarized light reflections from the polarimetric image, thereby generating an updated polarimetric image including generating a de-mosaicked imaged based on the identified polarized light reflections, wherein the identified polarized light reflections are ignored at de-mosaicking, and to identify a road feature including a lane marking based on the updated polarimetric image.

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: Optical surface degradation detection and remediation systems, devices, and methods are provided herein. An example method includes tracking a first object within images of an area of interest obtained over a period of time by an image sensor of a vehicle during vehicle movement; tracking a light source within the area of interest; determining a change in relative reflective intensity of the first object using the images, based on a light source angle formed between the light source and an optical plane of an optical surface the vehicle, the change in relative reflective intensity being indicative of veiling glare of the optical surface; and activating a remediating measure in response to the veiling glare.

Abstract: Method and apparatus are disclosed for autonomous vehicle systems utilizing vehicle-to-vehicle communication. An example vehicle includes a communication module configured to perform vehicle-to-vehicle (V2V) communication with an adjacent vehicle having an autonomous system. The example vehicle also includes a controller configured to monitor within the V2V communication for a request by the autonomous system for manual override and, upon identifying the request, determine a collision probability for the adjacent vehicle based at least on the V2V communication. The controller also is configured to compare the collision probability to a first threshold. The example vehicle also includes an autonomy unit to autonomously perform a defensive driving maneuver responsive to the controller determining that the collision probability is greater than the first threshold.

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.