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 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 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: 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.

Abstract: Method and apparatus are disclosed for monitoring of steering wheel engagement for autonomous vehicles. An example vehicle includes an autonomy unit configured to perform autonomous motive functions, a steering wheel, capacitive sensors coupled to the steering wheel, a second sensor configured to monitor an operator, and a controller. The controller is configured to detect a first heart rate via the capacitive sensors, detect a second heart rate via the second sensor, identify that an engagement-imitating device is coupled to the steering wheel responsive to determining that the first heart rate does not correlate with the second heart rate, and emit an alert responsive to determining that the engagement-imitating device is coupled to the steering wheel.

Abstract: Aerial vehicle sensor calibration systems and methods are provided herein. An example method includes determining a jarring event, determining when a drone is level relative to an aerial vehicle platform of a vehicle, the vehicle having a calibration controller, determining when the vehicle is level relative to a subordinate surface, and transmitting a signal to a drone controller by the calibration controller to calibrate a gyroscope or an accelerometer of the drone.

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 processor is programmed to: identify, based on data from first sensors included in a first vehicle, a second vehicle moving within a first threshold distance of a travel path of the first vehicle and within a second threshold distance of the first vehicle. The processor is further programmed to receive, from second sensors included in the first vehicle, transient velocity data of the second vehicle; and determine an operating condition of the second vehicle based on the transient velocity data.

Abstract: A method to transfer thermal energy from an interior area includes, among other things, automatically transmitting a request to open a door to provide an opening from the interior area. The opening permits thermal energy to move from the interior area to an exterior area. At least some of the thermal energy within the interior area is generated when charging a vehicle within the interior area. An assembly includes, among other things, a door actuation module that automatically transmits a request to open a door to provide an opening from an interior area. The opening permits thermal energy to move from the interior area to an exterior area. At least some of the thermal energy within the interior area is generated when charging a vehicle within the interior area.

Abstract: A computer-implemented method for using machine learning to determine control parameters for a control system, in particular of a motor vehicle, in particular for controlling a driving operation of the motor vehicle. The method includes: providing a set of travel trajectories; deriving reward functions from the travel trajectories, using an inverse reinforcement learning method; deriving driver type-specific clusters based on the reward functions; determining control parameters for a particular driver type-specific cluster.

Abstract: A system includes a computer comprising a processor and a memory. The memory stores instructions such that the processor is programmed to determine, based on vehicle lidar sensor data, a reflectivity of an area of a road surface, and to determine whether the area is wet based on the determined reflectivity of the area, a dry-condition reflectivity threshold for the area, and an angle of viewing the area from the lidar sensor.

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.

Abstract: A control system for a motor vehicle, for outputting a controlled variable, with the aid of which a directly controlled variable of a motor vehicle is adjustable via suitable control operations, in order to adapt the directly controlled variable to a reference variable of the control system. The control system includes a controller, which is configured to output a first output variable on the basis of the directly controlled variable of the motor vehicle, and on the basis of the reference variable of the control system. The control system further includes a predictive model, which may be trained to output a second output variable that reflects a deviation of a driving behavior of a driver of the motor vehicle from the first output variable of the controller. The controlled variable of the control system encompasses an addition of the first output variable and the second output variable.

Abstract: A computing system can be programmed to determine a location, speed and direction for a first vehicle. The computer can be further programmed to determine probabilities for predicted locations, speeds and directions of the first vehicle based on identifying the first vehicle and the driving environment. The computer can be further programmed to operate a second vehicle based on the determined probabilities for predicted locations, speeds and directions of the first vehicle.

Abstract: A computer-implemented method for inferring a device feature of a device produced on a wafer. The method includes: providing a wafer feature model associating a wafer position indicating a position of a produced device on the wafer to a device feature, wherein the wafer feature model is configured to be trained by one or more wafer feature maps and particularly configured as a Gaussian process model, providing a sample device feature of at least one device at a sample wafer position, and inferring the device feature of at least one other device of the wafer depending on the provided wafer feature model.