A method of an interactive server system for providing location information to an in-vehicle terminal includes receiving the location information and indication information from an interactive client, the indication information indicating address information of the in-vehicle terminal, obtaining the address information of the in-vehicle terminal based on the received indication information, sending the location information to the in-vehicle terminal according to the address information of the in-vehicle terminal, and instructing the in-vehicle terminal to generate a navigation path according to the location information.

This vehicle-mounted device installed in a vehicle is provided with a communication unit that communicates with a mobile terminal; a vehicle information acquiring unit that acquires operation information of the vehicle; and a vehicle-mounted controller that, on the basis of an operating status of the vehicle-mounted device, transmits a first notification notifying the mobile terminal of the possibility that the vehicle has stopped to the mobile terminal via the communication unit and that, on the basis of the operation information of the vehicle, transmits a second notification notifying the mobile terminal that the vehicle has stopped to the mobile terminal via the communication unit.

Some embodiments of the invention provide a novel prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for a user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following: (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user's calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user. In some embodiments, the prediction engine only relies on user-specific data stored on the device on which this engine executes. Alternatively, in other embodiments, it relies only on user-specific data stored outside of the device by external devices/servers. In still other embodiments, the prediction engine relies on user-specific data stored both by the device and by other devices/servers.

When the distance indicated by distance information received by a Bluetooth module (31) is less than a predetermined first reference distance (KY1), a guidance image (33) including a distance image (33a) and a direction image (33b) is generated, and this guidance image (33) is displayed on a combiner (26) of a helmet (20). By stopping the display of the distance image (33a) of the guidance image (33) and continuing to display the direction image (33b) from when the distance indicated by the distance information becomes less than a second reference distance (KY2) shorter than the first reference distance (KY1) to when the guidance point is reached, the driver's attention is focused on the direction image (33b), so that a mistake in the direction of travel at the guidance point can be prevented.

Apparatus, systems, and/or methods may provide travel assistance. For example, a determination may be made that a user is to begin driving in the near future. In addition, a determination may be made of one or more predicted destinations when the user is to begin driving in the near future. Thus, travel assistance may be provided to the user based on the one or more predicted destinations when the user is to begin driving in the near future.

Sentiment-based navigation is provided herein. A method can include extracting features of sensor data captured by a sensor associated with a vehicle, wherein the sensor data is representative of a subject selected from a group of subjects comprising an occupant of the vehicle and an environment in which the vehicle is located, resulting in extracted features. The method can further include determining sentiment data representative of an emotional condition of the occupant of the vehicle based on an analysis of the extracted features, and generating a navigation route for the vehicle from an origin point to a destination point based on the sentiment data.

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous vehicles may be automatically refueled by routing the vehicles to available fueling stations when not in operation, according to methods described herein. A fuel level within a tank of an autonomous vehicle may be monitored until it reaches a refueling threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to refuel the vehicle may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a fueling station, refill a fuel tank, and return to its starting location in order to refuel when not in use.

Systems and methods utilize an inertial navigation sub-system configured to determine a plurality of relative positions of the navigation system from a reference position based on the determined speed and/or acceleration and/or direction of the navigation system and/or changes thereto; and a position estimator sub-system configured to estimate the absolute position of the navigation system based on at least two received signals. A first track is defined based on the plurality of relative positions determined by the inertial navigation sub-system during a period of time and a second track is defined based on the plurality of estimates of the absolute position by calculating a best fit using the plurality of estimates of the absolute position, where the second track approximates a same shape as the first track.

Some embodiments of the invention provide a novel prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for a user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following: (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user’s calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user. In some embodiments, the prediction engine only relies on user-specific data stored on the device on which this engine executes. Alternatively, in other embodiments, it relies only on user-specific data stored outside of the device by external devices/servers. In still other embodiments, the prediction engine relies on user-specific data stored both by the device and by other devices/servers.

A method includes receiving one or more search terms at a mobile computing device while the mobile computing device is located at a particular location. A search query that includes the one or more search terms and a location history of the mobile computing device is transmitted to a server. The method also includes receiving one or more search results in response to the search query, where the one or more search results include content identified based on a predicted destination of the mobile computing device. An interface identifying the one or more search results is displayed at the mobile computing device.