Navigation data is received. The navigation data comprises a plurality of point of interest (POI) models. Each POI model is a representation of a POI. At least a portion of the plurality of POI models are analyzed based on a data extraction model to determine a link between two or more POIs based on POI models corresponding to the two or more POIs. The two or more POIs are located within a predefined distance of one another. An entity of interest (EOI) model is generated based on the POI models corresponding to the two or more POIs, the determined link, and the data extraction model. The EOI model is stored for use in performing one or more navigation functions.

Embodiments are directed towards providing a system that presents content to a user of a vehicle based on where the vehicle is going. A microphone captures audio signals within the vehicle, which are analyzed for route information. These audible commands may be said by a person in the vehicle, such as a passenger telling the driver where to turn, or they may be received from a mobile computing device, such as a smartphone executing a map application that is providing audible directions. An anticipated route of the vehicle is determined based on the audible route information. Content is selected and presented to the user of the vehicle based on the anticipated route. Images of a display screen of the mobile computing device may also be analyzed to identify the route information.

Disclosed is a method of operating a vehicle user experience (UX) control apparatus by executing an artificial intelligence (AI) algorithm and/or a machine learning algorithm in a 5G communication environment constructed for an Internet of things (IoT) network. The vehicle UX control method includes monitoring the interior of a vehicle to recognize an occupant, determining the type of occupant, providing a user interface corresponding to the occupant based on the type of occupant, and performing a process corresponding to a user request input by the occupant through the user interface.

Embodiments for providing navigation routes by one or more processors are described. An indication of a destination at a first location is received. A selection of a route initiating event is received. The route initiating event is detected. After the route initiating event is detected, a navigation route from a current location of a user to the destination is determined. No navigational guidance is provided to the user until the route initiating event is detected notwithstanding the user input the destination at the first location. An indication of the determined navigation route is generated thereafter.

A route searching method using a route searching device for searching for a first route to reach a destination includes accumulating a history of activities of a driver in a database, when the history of activities is accumulated, setting a region based on a position on a map at which the history of activities is accumulated as a first region, increasing an area of the first region as the history of activities increases, and searching for a secondary route for reaching the destination from the first region using the route searching device.

The technology relates to identifying and addressing aberrant driver behavior. Various driving operations may be evaluated over different time scales and driving distances. The system can detect driving errors and suboptimal maneuvering, which are evaluated by an onboard driver assistance system and compared against a model of expected driver behavior. The result of this comparison can be used to alert the driver or take immediate corrective driving action. It may also be used for real-time or offline training or sensor calibration purposes. The behavior model may be driver-specific, or may be a nominal driver model based on aggregated information from many drivers. These approaches can be employed with drivers of passenger vehicles, busses, cargo trucks and other vehicles.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting actions for an agent at a specific real-world location using historical data generated at the same real-world location. One of the methods includes determining a current geolocation of an agent within an environment; obtaining historical data for geolocations in a vicinity of the current geolocation of the agent from a database that maintains historical data for a plurality of geolocations within the environment, the historical data for each geolocation comprising observations generated at least in part from sensor readings of the geolocation captured by vehicles navigating through the environment; generating an embedding of the obtained historical data; and providing the embedding as an input to a policy decision-making system that selects actions to be performed by the agent.

The vehicle navigation device guides a user of a vehicle to a destination. The vehicle navigation device includes a car navigation system that acquires the destination, a head-up display that displays various information, and a body ECU that controls the information displayed on the head-up display based on the destination acquired by the car navigation system. The body ECU is configured to cause, on the assumption that the user alights from the vehicle at a current location, the head-up display to display post-alighting guidance information which is information for guiding the user to the destination regarding, for example, walking.

A navigation apparatus and a method for providing an individualization map service of the navigation apparatus are provided. The navigation apparatus includes a detector configured to sense a vehicle state and a travelling environment during travelling, and a processor configured to recognize a drive context based on the vehicle state and the travelling environment, and to make an individualization map to be serviced based on the recognized drive context.

This disclosure relates to method and system for providing personalized driving or navigation assistance. The method may include receiving sensory data with respect to a vehicle from a plurality of sensors and multi-channel input data with respect to one or more passengers inside the vehicle from a plurality of onboard monitoring devices, performing fusion of the sensory data and the multi-channel input data to generate multimodal fusion data, determining one or more contextual events based on the multi-modal fusion data using a machine learning model, wherein the machine learning model is trained using an incremental learning process and comprises a supervised machine learning model and an unsupervised machine learning model, analysing the one or more contextual events to generate a personalized driving recommendation, and providing the personalized driving recommendation to a driver passenger or a navigation device.