Abstract: An image processing method and an electronic device are provided. The image processing method includes: displaying a first interface, where the first interface includes a first control; detecting a first operation on the first control; obtaining a first image stream in response to the first operation, where the first image stream is an image stream of first color space; converting the first image stream into a second image stream of second color space according to a demosaicing algorithm; performing downsampling and resampling on the second image stream to obtain a third image stream, where the third image stream is an image stream of the first color space, and a size of the third image stream is less than that of the first image stream; and performing image processing on the third image stream to obtain a fourth image stream.

Abstract: Systems and methods are disclosed for providing predictive distance-to-empty (DTE) assessments for vehicles that can include electric, gas, or hybrid vehicles. An example method includes determining a plurality of learned parameters of vehicle operation of a vehicle based on a plurality of energy consumption parameters of the vehicle; determining a plurality of predictive parameters of vehicle operation of the vehicle selected from any combination of weather data or navigation data, the navigation data being determined relative to a planned route and applying a DTE function for the energy source, the DTE function utilizing the plurality of learned parameters of vehicle operation, and the plurality of predictive parameters of vehicle operation of the vehicle, based on a current capacity of the energy source to determine a DTE for the vehicle.

Abstract: Augmented reality displays for locating vehicles are disclosed herein. An example method includes determining a current location of a mobile device associated with a user, determining a current location of a vehicle, and generating augmented reality view data that includes a first arrow that identifies a path of travel for the ridehail user towards the vehicle. The path of travel is based on the current location of the mobile device and the current location of the vehicle. The first arrow is combined with a view obtained by a camera of the mobile device or a view obtained by a camera of the vehicle.

Abstract: Systems and methods are disclosed for providing predictive distance-to-empty (DTE) assessments for vehicles that can include electric, gas, or hybrid vehicles. An example method includes determining a plurality of learned parameters of vehicle operation of a vehicle based on a plurality of energy consumption parameters of the vehicle; determining a plurality of predictive parameters of vehicle operation of the vehicle selected from any combination of weather data or navigation data, the navigation data being determined relative to a planned route and applying a DTE function for the energy source, the DTE function utilizing the plurality of learned parameters of vehicle operation, and the plurality of predictive parameters of vehicle operation of the vehicle, based on a current capacity of the energy source to determine a DTE for the vehicle.

Abstract: A server includes a processor, programmed to receive a vehicle location from a vehicle, receive a weather condition of the vehicle location from a cloud server, responsive to receiving a calendar of a user including an event, calculate a departure time based on the vehicle location and a location of the event, responsive to receiving a desired cabin temperature of the user from a mobile device, calculate a minimum time for the vehicle to reach the desired cabin temperature based on the weather condition, calculate a vehicle start time using the departure time and the minimum time, and send an instruction to the vehicle to start at the vehicle start time.

Abstract: Systems and methods are disclosed for providing predictive distance-to-empty (DTE) assessments for vehicles that can include electric, gas, or hybrid vehicles. An example method includes determining a plurality of learned parameters of vehicle operation of a vehicle based on a plurality of energy consumption parameters of the vehicle; determining a plurality of predictive parameters of vehicle operation of the vehicle selected from any combination of weather data or navigation data, the navigation data being determined relative to a planned route and applying a DTE function for the energy source, the DTE function utilizing the plurality of learned parameters of vehicle operation, and the plurality of predictive parameters of vehicle operation of the vehicle, based on a current capacity of the energy source to determine a DTE for the vehicle.

Abstract: This disclosure describes systems and methods for determining tire inflation pressure loss. An example method may include receiving first tire pressure data for a first tire of a vehicle at a first time. The example method may also include receiving second tire pressure data for the first tire at a second time. The example method may also include identifying a slow leak in the first tire based on a relationship between the first tire pressure data and the second tire pressure data. The example method may also include causing to send an alert to a user.

Abstract: Augmented reality displays for locating vehicles are disclosed herein. An example method includes determining a current location of a mobile device associated with a user, determining a current location of a vehicle, and generating augmented reality view data that includes a first arrow that identifies a path of travel for the ridehail user towards the vehicle. The path of travel is based on the current location of the mobile device and the current location of the vehicle. The first arrow is combined with a view obtained by a camera of the mobile device or a view obtained by a camera of the vehicle.

Abstract: A server includes a processor, programmed to receive a vehicle location from a vehicle, receive a weather condition of the vehicle location from a cloud server, responsive to receiving a calendar of a user including an event, calculate a departure time based on the vehicle location and a location of the event, responsive to receiving a desired cabin temperature of the user from a mobile device, calculate a minimum time for the vehicle to reach the desired cabin temperature based on the weather condition, calculate a vehicle start time using the departure time and the minimum time, and send an instruction to the vehicle to start at the vehicle start time.

Abstract: Systems and methods are disclosed for providing predictive distance-to-empty (DTE) assessments for vehicles that can include electric, gas, or hybrid vehicles. An example method includes determining a plurality of learned parameters of vehicle operation of a vehicle based on a plurality of energy consumption parameters of the vehicle; determining a plurality of predictive parameters of vehicle operation of the vehicle selected from any combination of weather data or navigation data, the navigation data being determined relative to a planned route and applying a DTE function for the energy source, the DTE function utilizing the plurality of learned parameters of vehicle operation, and the plurality of predictive parameters of vehicle operation of the vehicle, based on a current capacity of the energy source to determine a DTE for the vehicle.

Abstract: Systems, methods, and computer-readable media are disclosed for optimizing vehicle scheduling in routes for fleet management. Example methods may include determining traffic data associated with one or more routes for at least one first vehicle of a fleet and determining one or more parameters from the traffic data; performing at least one first artificial intelligence (AI)-based algorithm using the one or more parameters to determine a social travel time associated with one or more second vehicles; and performing at least one second AI-based algorithm based at least in part on the social travel time to determine one or more impact parameters.

Abstract: This disclosure describes systems, methods, and computer-readable media related to fuel consumption-based driving behavior scoring. Driving data may be obtained during operation of a vehicle. Based on braking data, a braking event during a first time period may be identified, wherein the braking event exceeds a braking threshold. Based on the braking event, first coached driving data may be generated. Based on the first coached driving data, first fuel savings may be determined. Based on acceleration data, an acceleration event during a second time period may be identified, wherein the acceleration event exceeds an acceleration threshold. Based on the acceleration event, second coached driving data may be generated. Based on the second coached driving data, second fuel savings may be determined. Based on the first and second fuel savings, a total fuel savings may be determined, and a driving behavior score may be generated.

Abstract: Method and apparatus are disclosed to facilitate TPMS broadcast mode selection. An example vehicle comprises sensors, a processor, and memory. The sensor are to generate vehicle dynamics information (VDI). The processor is in communication with the memory and with the sensors. The processor is configured to: estimate relative pitch and roll angles using the VDI, estimate a tire normal force using the relative pitch and roll angles and the VDI, estimate a tire rolling resistance using the tire normal force, and estimate a tire longitudinal force and a tire lateral force based on the tire rolling resistance and the VDI.