Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

At least a method for determining risk behavior of a driver is described. While a vehicle is being driven, data is obtained related to the position and movement of a wireless communications device. The data may indicate the type of behavior exhibited by the driver while the vehicle is being driven.

At least a method for determining risk behavior of a driver is described. While a vehicle is being driven, data is obtained related to the position and movement of a wireless communications device. The data may indicate the type of behavior exhibited by the driver while the vehicle is being driven.

A technique for enabling efficient retrieval of a digital representation of personality data of a user (402) by a client device (406) from a server (404) is disclosed, wherein the digital representation of the personality data is processed at the client device (406) to provide a user-adapted service to the user (402). A method implementation of the technique is performed by the server (404) and comprises storing a neural network being trained to compute personality data of a user based on input obtained from the user (402), receiving, from the client device (406), a request for a digital representation of personality data for a user (402), and sending, to the client device (406), the requested digital representation of the personality data of the user (402), wherein the personality data of the user is computed using the neural network based on input obtained from the user (402).

A technique for enabling efficient retrieval of a digital representation of personality data of a user (402) by a client device (406) from a server (404) is disclosed, wherein the digital representation of the personality data is processed at the client device (406) to provide a user-adapted service to the user (402). A method implementation of the technique is performed by the server (404) and comprises storing a neural network being trained to compute personality data of a user based on input obtained from the user (402), receiving, from the client device (406), a request for a digital representation of personality data for a user (402), and sending, to the client device (406), the requested digital representation of the personality data of the user (402), wherein the personality data of the user is computed using the neural network based on input obtained from the user (402).

A control system for an electric vehicle configured to simulate an engine stall which might occur in conventional vehicles while preventing the simulation of the engine stall in an unfavorable situation. A controller of the control system is configured to: execute an engine stall control to simulate a behavior of the conventional vehicle in a situation where an engine stall occurs by stopping a motor, when a virtual engine speed calculated by a virtual engine speed calculator falls below a predetermined speed; and execute a hold assist control to apply a brake torque to the wheel by the brake device upon execution of the engine stall control.

A control system for an electric vehicle configured to simulate an engine stall which might occur in conventional vehicles while preventing the simulation of the engine stall in an unfavorable situation. A controller of the control system is configured to: execute an engine stall control to simulate a behavior of the conventional vehicle in a situation where an engine stall occurs by stopping a motor, when a virtual engine speed calculated by a virtual engine speed calculator falls below a predetermined speed; and execute a hold assist control to apply a brake torque to the wheel by the brake device upon execution of the engine stall control.

Described herein are systems and methods for applying machine learning to telematics data to generate a unique vehicle fingerprint by periodically receiving telematics data generated at a plurality of sensors of a vehicle; standardizing the telematics data; aggregating the standardized telematics data; applying a trained machine learning model to embed the aggregated telematics data into a low-dimensional state; and generating a unique vehicle fingerprint, the vehicle fingerprint comprising a static component, a dynamic component, or both a static component and a dynamic component; including iterative repetition to update the dynamic component of the vehicle fingerprint.

An information processing apparatus, a vehicle, and an information processing method each capable of improving the visibility of an on-board meter panel. Information processing apparatus includes information acquirer that acquires traveling information on traveling of a vehicle including an on-board meter panel; and display pattern changer that changes a display pattern of the on-board meter panel in accordance with change in the traveling information.

An information processing apparatus, a vehicle, and an information processing method each capable of improving the visibility of an on-board meter panel. Information processing apparatus includes information acquirer that acquires traveling information on traveling of a vehicle including an on-board meter panel; and display pattern changer that changes a display pattern of the on-board meter panel in accordance with change in the traveling information.