A device that includes a housing configured to couple to an interior of a vehicle. The device further includes a card reader and a user interface. The card reader is configured to receive payment card information for a payment card. The user interface is configured to receive a user input identifying a product, to determine a current location of the device, to determine a vending location where the product is available based on the current location of the device, and to modify a route to a final destination to include the vending location. The user interface is further configured to send an authorization request requests a purchase of the product, to receive an approval message, to generate an authorization token that authorizes a purchase to retrieve the products from the vending location, and output the authorization token.

A system for preventing abnormal acceleration of a vehicle includes a device configured such that in a state in which the vehicle is stopped while the vehicle is turned on, the device is configured to determine whether abnormal acceleration prevention is necessary based on a final destination or a current position of the vehicle before the vehicle is turned off to thereby limit a vehicle speed. The system and a method for preventing abnormal operation of the vehicle can prevent a vehicle accident due to the abnormal acceleration caused by misoperation by a driver, such as misoperation of an accelerator pedal of the vehicle.

The technology relates to providing visually-verifiable metadata in response to a query. A query may be sent from an application. In response to the query visually-verifiable metadata corresponding to one or more points of interest relevant to the query and an image associated with the visually-verifiable metadata may be received. The image associated with the visually-verifiable metadata may be displayed and the visually-verifiable metadata may be annotated within the displayed image.

Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.

Systems and methods are provided for suggesting a charging station for an electric vehicle. A partial range of the electric vehicle corresponding to a predetermined percentage of the current state of charge of a battery of the electric vehicle is determined. A location corresponding to the partial range, along a route to a destination, is determined and used to select a suggested charging station based on the location corresponding to the partial range. The suggested charging station is generated for presentation at a display.

A processing apparatus includes a control unit. The control unit is configured to acquire facility information containing an advertisement or publicity on a facility located along a travel route that a vehicle is scheduled to travel or a facility located within a predetermined range from the travel route, and, while the vehicle is traveling along the travel route, process an image of a first facility associated with the facility information or an image of a second facility present around the first facility based on the facility information and display the image of the first facility or the image of the second facility on a display provided in the vehicle.

An approach is provided for providing mobility insight data related to shared vehicles for a point of interest (POI). The approach involves retrieving shared vehicle data for the POI. The shared vehicle data indicates one or more shared vehicle events that have occurred, that are occurring at a given time, or a combination thereof within a threshold proximity of the POI. The approach also involves processing the shared vehicle data to determine mobility insight data. The mobility insight data includes a shared vehicle usage pattern, a shared vehicle availability pattern, or a combination thereof under one or more contexts for travel to or from the POI. The approach further involves presenting the mobility insight data in a location-based user interface.

A system includes a GPS module, user interface module, database and routing engine. To find nearby POIs in one embodiment the routing engine identifies POIs in the database located within a threshold distance from the planned route in Euclidean distance. The routing engine filters the results by exploring the road network to determine whether each of the POIs is within the threshold distance of the route in driving distance. The filtering may include exploring outward from the POI, until all roads within the threshold distance are explored or the route is reached; alternatively, distance to the POIs is checked by exploring outward from the route until all roads within the threshold distance of the route have been exposed. For those POIs reached in the exploration, the nearest point on the route and the distance to that point are known. Similar searches are performed based on driving time rather than distance.

A system for navigating a vehicle automatically from a current location to a destination location without a human operator is provided. The system of the vehicle includes a global positioning system (GPS) for identifying a vehicle location and a communications system for communicating with a server of a cloud system. The server is configured to identify that the vehicle location is near or at a parking location. The communications system is configured to receive mapping data for the parking location from the server, and the mapping data is at least in part used to find a path at the parking location to avoid a collision of the vehicle with at least one physical object when the vehicle is automatically moved at the parking location. The mapping data is processed by electronics of the vehicle so that when the vehicle is automatically moved collision with the at least one physical object is avoided and the electronics of the vehicle is configured to process a combination of sensor data obtained by sensors of the vehicle. The processing of the sensor data uses image data obtained from one or more cameras and light data obtained from one or more optical sensors.

A method of operation of a navigation system includes: determining a POI (point-of-interest) based on comparing a destination description and a POI information for a match; determining a privacy preference for communicating with a device; generating a temporary contact based on the POI and the privacy preference representing an anonymous mode; and establishing a communication session between a contact address and the temporary contact with a control unit based on the privacy preference for communicating with the POI.