An automated driving vehicle (200) includes a communication device (220), a biometric information acquirer (240), and an automated driving controller (250). The communication device (220) is configured to transmit biometric information acquired by the biometric information acquirer (240) to an external device and receives a response signal including attribute information for the transmitted biometric information. The automated driving controller (250) is configured to execute automated driving according to route information formed on the basis of the attribute information included in the received response signal.

A system includes at least one non-transitory storage medium storing a set of instructions and at least one processor in communication with the at least one non-transitory storage medium. When executing the set of instructions, the at least one processor may be directed to cause the system to obtain a service request signal sent from a user terminal via wireless communication, wherein the service request signal encodes identifier data, a first departure location, and a first departure time; retrieve one or more historical records related to the identifier data, wherein a historical record includes a historical departure location, historical departure time and a historical destination location; determine, using a pre-stored destination matching algorithm, a selection probability of the one or more historical destination location; determine, based on the selection probability, a suggested destination location, which is the same as the one or more historical destination locations.

A method for controlling a vehicle can include determining a driver state based on physiological response of an operator and navigational irregularities from observed driving patterns. The physiological response may indicate observed driver stress based on bodily responses that can include respiration, heart rate, ocular movement, or other stress indicators. The method further includes determining a vehicle route having a trip start position, a path to a present position, and a trip destination, and identifying a navigation irregularity based on the vehicle route, the driver state, and a historic record driving patterns. The method may include displaying a navigation assistant output on a heads-up Human Machine Interface (HMI) based on the navigation irregularity and the physiological response of the user. The system may provide user-selectable navigation assistance including placing a phone call to a family member for navigation guidance, providing turn-by-turn navigation guidance via the heads-up HMI, and/or other measures.

Route management methods and systems for electric vehicles are provided. First, a driving monitoring system of an electric vehicle is used to detect a driving state of the electric vehicle. Then, the driving state is transmitted to the server via a network by the driving monitoring system. The server performs an analysis according to the driving state to obtain a driving behavior index of a driver corresponding to the electric vehicle. Then, a plurality of routes are provided in the server, wherein each route includes a plurality of stations, and average distance between two stations. The server selects one of the routes according to the driving behavior index, and assigns the selected route to the driver of the electric vehicle.

A server device includes a communication unit and a control unit that send and receives information to and from another device via the communication unit. The control unit receives, from a plurality of terminal devices, information indicating a boarding point at which a user of each of the terminal devices desires to board a vehicle, sends, to the vehicle, information for causing the vehicle to travel along a route via a nearby region of each of a plurality of the boarding points while satisfying a predetermined condition, and notifies the terminal devices of the route.

A processor-implemented method includes storing, for each of multiple road sections, section-specific road-nonlinearity values relating to road-nonlinearity characteristics of the respective road section. The method includes receiving, from a user, user-desired route-specific road-nonlinearity values relating to the road-nonlinearity characteristics. The method further includes calculating, from the stored section-specific road-nonlinearity values, for a candidate route, one or one route-specific road-nonlinearity values relating to the road-nonlinearity characteristics. The candidate route's route-specific road-nonlinearity values are compared to the user-desired route-specific road-nonlinearity values. Based on results of the comparison, the candidate route is select to be a recommended route. The recommended route is communicated to the user.

An embodiment takes the form of a system that obtains application-usage data from a personal mobile device of a vehicle operator of a vehicle. The application-usage data reflects a usage, during operation of the vehicle, of an application on the personal mobile device. The system identifies a vehicle feature, of the vehicle, that provides a vehicle functionality similar to an application functionality provided by the application on the personal mobile device, and performs a comparison of the obtained application-usage data with feature-usage data. The feature-usage data reflects a usage, during operation of the vehicle, of the identified vehicle feature. The system determines, based on the comparison, a usage preference for the application during operation of the vehicle over the identified vehicle feature.

A system for generating guidance information for a driver includes a processor and a storage device. The storage device stores guidance history of the driver. The processor generates a feature value based on match/mismatch of guidance types abstracted from the guidance history. The processor determines a guidance type for the driver on the basis of the feature value. The processor generates information for issuing guidance of the determined guidance type to the driver according to driver monitoring information.

In some implementations, a system may identify, using a machine learning model, a pattern of events based on account information associated with a user. The system may generate a prediction of a time period during which a predicted event, associated with the pattern of events, is predicted to occur. The system may receive an indication that a vehicle, associated with the user, is to travel a route from a starting location to an ending location during the time period. The system may identify a physical location associated with the predicted event based on a location associated with the vehicle or based on the route. The system may generate a new route that includes the starting location, the physical location as a waypoint along the new route, and the ending location. The system may transmit information that identifies the new route to a device associated with the vehicle.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for customizing a user experience for users of a transportation matching system. In particular, in one or more embodiments, the disclosed systems can detect a mobile application session on a requester device, determine information related to a location of a requester device, and then utilize one or more modifications to modify a user interface experience for the mobile application session on the requester device.