Abstract: Devices, systems, and methods for enhancing color consistency in 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, the second light, and a distance between the camera and the vehicle light, second color values for the pixels of the first image data; generating second image data based on the second color values; and presenting the second image data.

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: Use of embedded metadata for data privacy compliance is provided. In a data store, self-managed data is maintained including metadata specifying retention policy data. Responsive to a self-update to scrub PII from the self-managed data being indicated by the retention policy data, the PII is removed from the self-managed data maintained by the data store. Responsive to a self-update to delete the self-managed data from the self-managed data being indicated by the retention policy data, the self-managed data is removed from the data store.

Abstract: Methods and systems are provided for selectively inhibiting a stop-start controller of a vehicle based on a predicted anxiety of a driver of the vehicle on a road segment that includes one or more road attributes associated with increased levels of anxiety. In one example, selectively inhibiting a stop-start controller of a vehicle based on a predicted anxiety of a driver of the vehicle includes determining a driver classification for a driver operating the vehicle; predicting an anxiety level of the driver at an upcoming traffic condition, based on the driver classification; and selectively inhibiting an upcoming engine idle-stop event based on the predicted anxiety level of the driver.

Abstract: Handling personally identifiable information (PII) in data streams is provided. Processed sensor data is received, from a plurality of vehicles including sensors capturing raw sensor data, the raw sensor data including captured PII and non-PII. The processed sensor data includes simulated PII created based on the captured PII and one or more layers of the captured PII corresponding to the simulated PII. A request is received from a client device for a portion of the processed sensor data. Access keys corresponding to the request are identified. A result is constructed according to the access keys using the processed sensor data. The constructed result is sent to the client device responsive to the request.

Abstract: Ensuring privacy consent for handling of occupant vehicle data is provided. A feature identification vector indicative of an identity of a vehicle occupant of a vehicle is identified. The feature identification vector is used to identify whether consent for use of vehicle data was provided by the vehicle occupant. The consent is requested responsive to the identity of the vehicle occupant not having consented to data collection. Responsive to the consent being given by the vehicle occupant, the consent and the feature identification vector of the vehicle occupant is stored in a storage of the vehicle. The vehicle data is uploaded in accordance with whether the consent was granted for the vehicle occupant.

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 vehicle may determine that it is stopped and detect a perimeter-modification event predefined as correlating to a changed vehicle perimeter. The vehicle may further, responsive to the perimeter-modification event, define an expanded vehicle perimeter, larger than a perimeter predefined as representing the vehicle in travel. The perimeter expansion may be done in accordance with a predefined modification associated with the detected perimeter modification event. The vehicle may additionally wirelessly share the expanded vehicle perimeter with at least one other vehicle.

Abstract: Systems, methods, and computer-readable media are disclosed for automated multimodal delivery. Example methods may include determining information associated with the delivery; determining, based on at least a first portion of the information, that a first confidence level indicative of a delivery capability using a first vehicle is above a first threshold; and determining, based on at least a second portion of the information, a second confidence level indicative of a delivery preference for delivery using the first vehicle relative to a second vehicle.

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 damage quantifier for a vehicle component formed of composite material is determined based on vehicle sensor data. The vehicle is operated based on a mission determined according to determined damage quantifier.

Abstract: This disclosure is generally directed to systems and methods for detecting distracted driving. In an example method, a light source identification procedure may be executed for detecting a first device present inside a vehicle and outside a field of view of a driver monitoring camera (such as upon a dashboard). The procedure can include evaluating an image data sample obtained from the driver monitoring camera so as to identify various devices that may be emitting light incident upon the face of the driver. The incident light may include a first pulse pattern corresponding to light emitted by the first device and a second pulse pattern corresponding to light emitted by a second device. The first device can be detected in various ways such as by evaluating a phase relationship between the first pulse pattern and the second pulse pattern and/or by evaluating spectral content present in the image data sample.

Abstract: A computer comprises a processor and a memory. The memory stores instructions executable by the processor to, upon detecting an airbag deployment in a vehicle, determine an airbag inflation state based on vehicle sensor data, to adjust a calibration parameter of a vehicle radar sensor based on the determined airbag inflation state, to operate the vehicle radar sensor based on the adjusted calibration parameter, and to update an output characteristic of the radar sensor for operating the vehicle based on the determined airbag inflation state.

Abstract: Method and apparatus are disclosed for vehicle-rendering generation for vehicle display based on short-range communication. An example vehicle includes a communication module configured to wirelessly communicate with remote sensing devices, a display configured to present a vehicle rendering of the vehicle, and a controller. The controller is configured to, in response to detecting an activation event, collect sensing data via the communication module. The controller also is configured to determine whether an object is coupled to the vehicle based on the sensing data and, in response to detecting an object coupled to the vehicle, modify the vehicle rendering to include an object rendering of the object.

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: Methods and systems are provided for selectively inhibiting a stop-start controller of a vehicle based on a predicted anxiety of a driver of the vehicle on a road segment that includes one or more road attributes associated with increased levels of anxiety. In one example, selectively inhibiting a stop-start controller of a vehicle based on a predicted anxiety of a driver of the vehicle includes determining a driver classification for a driver operating the vehicle; predicting an anxiety level of the driver at an upcoming traffic condition, based on the driver classification; and selectively inhibiting an upcoming engine idle-stop event based on the predicted anxiety level of the driver.

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, comprising a computer that includes a processor and a memory, the memory storing instructions executable by the processor to input second vehicle sensor data received via one or more of vehicle-to-vehicle (V-to-V) and vehicle-to-infrastructure (V-to-I) networking into a generative query neural network (GQN) trained to generate second vehicle viewpoint data and predict a path for a second vehicle based on the second vehicle viewpoint data with a reinforcement learning deep neural network (RLDNN). The computer can be further programmed to obtain a confidence for the predicted second vehicle path from a Bayesian framework applied to the RLDNN and determine a first vehicle path based on the predicted second vehicle path and the confidence.

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.