Abstract: A lockable package delivery locker at a publicly accessible location facilitates secure delivery by a delivery user of a package for later pickup by a recipient user. While the package is inside the locker a disinfection system disinfects the package and potentially its contents. A plurality of disinfection systems may provide different types of disinfection, including UV, chemical, nanotechnology, based on the type of packaging material. An electronic message may facilitate positioning UV emitters or chemical dispersion nozzles relative to the package. A locking mechanism and the disinfection system(s) may be controllable remotely from a server to which a computer device of the locker is in communication with, locally via an application on a UE device, or manually via an interface of the locker. The delivery user may be an individual shipping the package or a delivery service personnel, with the recipient user typically being the other.

Abstract: A lockable package delivery locker at a publicly accessible location facilitates secure delivery by a delivery user of a package for later pickup by a recipient user. While the package is inside the locker a disinfection system disinfects the package and potentially its contents. A plurality of disinfection systems may provide different types of disinfection, including UV, chemical, nanotechnology, based on the type of packaging material. An electronic message may facilitate positioning UV emitters or chemical dispersion nozzles relative to the package. A locking mechanism and the disinfection system(s) may be controllable remotely from a server to which a computer device of the locker is in communication with, locally via an application on a UE device, or manually via an interface of the locker. The delivery user may be an individual shipping the package or a delivery service personnel, with the recipient user typically being the other.

Abstract: A controller/application analyzes image data from a camera to determine the distance to an object in an image based on the size of the object in the image and based on a known focal length of a camera that captured the image and based on a known dimension of the actual object. The known dimension of the object may be retrieved from a database that is indexed according to outline shape, color, markings, contour, or other similar features or characteristics. The distance determined from analysis of the image and objects therein may be used to calibrate, or to verify the calibration of, complex distance determining systems that may include LIDAR. Object distance determinations in different image frames, whether to the same or different object, taken a known time apart may be used to determine speed of the camera that took the images, or speed of a vehicle associated with camera.

Abstract: Techniques described herein may be used to provide a driver of a vehicle with an accurate assessment of the remaining life of the vehicle battery. An on-board device may collect information from one or more sensors or devices within the vehicle. The information may be processed to generate a data set that accurately describes the current status and operating conditions of the battery. The data set may be used to evaluate the health of the battery and make predictions regarding the future performance of the battery, which may be communicated to the driver of the vehicle. Machine-learning techniques may be implemented to improve upon methodologies to evaluate the health of the battery and make predictions regarding battery performance.

Abstract: Information generated by human behavior detection sensors (i.e., cameras, microphones, pressure sensors, wearables), and vehicle operational parameter information train a machine learning model to determine a driver's emotional state based on vehicle operational parameter information. The training information may be transmitted by a wireless device for each of a fleet of vehicles and their human driver during a training period.

Abstract: A controller/application analyzes image data from a camera to determine the distance to an object in an image based on the size of the object in the image and based on a known focal length of a camera that captured the image and based on a known dimension of the actual object. The known dimension of the object may be retrieved from a database that is indexed according to outline shape, color, markings, contour, or other similar features or characteristics. The distance determined from analysis of the image and objects therein may be used to calibrate, or to verify the calibration of, complex distance determining systems that may include LIDAR. Object distance determinations in different image frames, whether to the same or different object, taken a known time apart may be used to determine speed of the camera that took the images, or speed of a vehicle associated with camera.

Abstract: A device may receive a request for imaging of a particular location. The device may identify one or more sensors associated with imaging the particular location. The device may select a sensor, of the one or more sensors, for imaging the particular location. The sensor may be associated with an autonomous vehicle. The device may cause the autonomous vehicle to move the sensor to the particular location. The device may receive imaging of the particular location based on causing the autonomous vehicle to move the sensor to the particular location. The device may selectively combine the imaging of the particular location with archived other imaging of the particular location. The device may provide imaging of the particular location to fulfill the request for imaging of the particular location based on selectively combining the imaging of the particular location with archived imaging of the particular location.

Abstract: Information generated by human behavior detection sensors (i.e., cameras, microphones, pressure sensors, wearables), and vehicle operational parameter information train a machine learning model to determine a driver's emotional state based on vehicle operational parameter information. The training information may be transmitted by a wireless device for each of a fleet of vehicles and their human driver during a training period.

Abstract: A controller/application synchronizes a camera's image capture rate with a LIDAR light burst rate. The controller instructs LIDAR to direct bursts within a field of view of the camera. When reflection of energy of a given burst is detected, the controller/application instructs an image recognition/analysis application to determine object characteristics by evaluating pixels of an image corresponding to the given burst within an evaluation range that corresponds to the location to which the burst was directed during an aperture open period when the camera captured the image. The controller may also instruct the LIDAR to determine a distance to an object that reflected the energy of the given burst. Based on the determined distance to the object and object characteristics, the controller may generate a take-action message. The take-action message may include instruction for controlling an autonomous vehicle to avoid interaction with the object that reflected energy of the burst.

Abstract: A controller/application synchronizes a camera's image capture rate with a LIDAR light burst rate and direction. A user may identify and manually bound an object-of-interest in an image captured with the camera with a user interface. Image and LIDAR point cloud data corresponding to the object-of-interest are applied to a machine learning model to train the model to automatically identify and bound objects-of-interest in future images based on point cloud data corresponding to the future images without human intervention. The LIDAR point cloud data and corresponding image data from the automatically identifying and bounding of objects are applied to the trained model to result in a refined trained machine learning model. The refined machine learning model may be used to determine the nature, location, distance, motion, and heading of an object of interest in an image by evaluation of the image without using corresponding LIDAR point cloud data.

Abstract: Aerial images, such as images from satellites or other aerial imaging devices, may be used to assist in responding to the occurrence of events (such as vehicle accidents or other emergency events) or conditions. In one implementation, an alert may be received indicating a condition or event associated with a user device. In response, an aerial image associated with the location of the user device may be requested. The alert may be responded to based on the received image. Aerial imaging may also provide views of the road ahead of a driver that terrain, topography, or darkness may otherwise impede. Image recognition may provide analysis of a hazard, condition, or occurrence at a scene that the aerial imaging system has captured and transmitted in response to a request.

Abstract: A puffer device receives a puffer trigger instruction signal from a computer device that processes signals generated by one or more sensors associated with a vehicle, such sensors including cameras focused on a drivers face or head, cameras directed at lane markers, or motion sensors that may be worn by the driver or included in an article worn by the driver. When the computer device determines that a danger condition has occurred, such as the driver being drowsy or veering out of a travel lane, the puffer device orients itself based on the detected location of the driver's head and puffs air at the drivers head or face to alert or awaken him or her. The puffer trigger instruction signal may include instructions to direct additional puffs, possibly of different velocity, length, or frequency, at the driver if the danger condition, or conditions, continue to exist after a first puff.

Abstract: A system comprises a weather data network configured receive a severe weather profile including severe weather duration data, severe weather geographic boundary data, and severe weather type data. The system receives an automotive sensor profile for at least one individual vehicle including individual vehicle geographic location data. The system determines and stores a vehicle exposure profile for the at least one individual vehicle including proximity of said individual vehicle geographic location data to the severe weather geographic boundary data.

Abstract: A computer device may include logic configured to detect a wake up event that wakes the computer device from an idle mode, wherein the wake up event indicates that a user is getting ready to use a vehicle; obtain accelerometer data from an accelerometer, associated with the vehicle, during a time period that includes the wake up event; determine a number of door slam events during the time period based on the obtained accelerometer data; and determine a number of people in the vehicle based on the determined number of door slam events.

Abstract: A method for providing crowd sourced navigation alerts including receiving, at a User Equipment (UE), an Multimedia Broadcast Multicast Service (MBMS) start session message when the UE is located within a predefined MBMS area. The method may further include receiving traffic message data, which includes information regarding at least one traffic event, over the MBMS session, where the traffic message data may be based on at least one traffic notification provided by at least one second UE. The method may also include comparing a distance from the UE to a position of the at least one traffic event described in the traffic message data to at least one threshold, and providing at least one alert associated with the at least one traffic event based on the comparing.

Abstract: Techniques described herein may be used to provide a driver of a vehicle with an accurate assessment of the remaining life of the vehicle battery. An on-board device may collect information from one or more sensors or devices within the vehicle. The information may be processed to generate a data set that accurately describes the current status and operating conditions of the battery. The data set may be used to evaluate the health of the battery and make predictions regarding the future performance of the battery, which may be communicated to the driver of the vehicle. Machine-learning techniques may be implemented to improve upon methodologies to evaluate the health of the battery and make predictions regarding battery performance.

Abstract: A computer device may include logic configured to detect a wake up event that wakes the computer device from an idle mode, wherein the wake up event indicates that a user is getting ready to use a vehicle; obtain accelerometer data from an accelerometer, associated with the vehicle, during a time period that includes the wake up event; determine a number of door slam events during the time period based on the obtained accelerometer data; and determine a number of people in the vehicle based on the determined number of door slam events.

Abstract: A device may receive a request for imaging of a particular location. The device may identify one or more sensors associated with imaging the particular location. The device may select a sensor, of the one or more sensors, for imaging the particular location. The sensor may be associated with an autonomous vehicle. The device may cause the autonomous vehicle to move the sensor to the particular location. The device may receive imaging of the particular location based on causing the autonomous vehicle to move the sensor to the particular location. The device may selectively combine the imaging of the particular location with archived other imaging of the particular location. The device may provide imaging of the particular location to fulfill the request for imaging of the particular location based on selectively combining the imaging of the particular location with archived imaging of the particular location.

Abstract: Aerial images, such as images from satellites or other aerial imaging devices, may be used to assist in responding to the occurrence of events (such as vehicle accidents or other emergency events) or conditions. In one implementation, an alert may be received indicating a condition or event associated with a user device. In response, an aerial image associated with the location of the user device may be requested. The alert may be responded to based on the received image. Aerial imaging may also provide views of the road ahead of a driver that terrain, topography, or darkness may otherwise impede. Image recognition may provide analysis of a hazard, condition, or occurrence at a scene that the aerial imaging system has captured and transmitted in response to a request.

Abstract: A method for providing crowd sourced navigation alerts including receiving, at a User Equipment (UE), an Multimedia Broadcast Multicast Service (MBMS) start session message when the UE is located within a predefined MBMS area. The method may further include receiving traffic message data, which includes information regarding at least one traffic event, over the MBMS session, where the traffic message data may be based on at least one traffic notification provided by at least one second UE. The method may also include comparing a distance from the UE to a position of the at least one traffic event described in the traffic message data to at least one threshold, and providing at least one alert associated with the at least one traffic event based on the comparing.