A device and a method are provided. The device includes a communication module, a memory, and a processor. The processor is configured to acquire position information of a vehicle via the communication module, determine whether high definition (HD) map information corresponding to the position information is acquired, display three-dimensional (3D) navigation information in augmented reality by using the HD map information when the HD map information is acquired, and display two-dimensional (2D) navigation information in augmented reality when the HD map information is not acquired. The 3D navigation information is information in which virtual 3D graphic information for driving guidance is spatially matched to and displayed on an actual object in the real world by using the HD map information, and the 2D navigation information is information in which virtual 2D graphic information for driving guidance is planarly matched to and displayed on an actual object in the real world.

A device may receive vehicle operation data associated with operation of a plurality of vehicles, and may process the vehicle operation data to generate processed vehicle operation data. The device may extract multiple features from the processed vehicle operation data, and may train machine learning models, with the multiple features, to generate trained machine learning models that provide model outputs. The device may process the multiple features, with a feature selection model and based on the model outputs, to select sets of features from the plurality of features, and may process the sets of features, with the trained machine learning models, to generate indications of driving behavior and reliabilities of the indications. The device may select a set of features, from the sets of features, based on the indications and the reliabilities, where the set of features may be calculated by a device associated with a particular vehicle.

Methods and systems for determining risk associated with operation of autonomous vehicles using autonomous communication are provided. According to certain aspects, autonomous operation features associated with a vehicle may be determined, including features associated with autonomous communication between vehicles or with infrastructure. This information may be used to determine risk levels for a plurality of features, which may be based upon test data regarding the features or actual loss data. Expected use levels and autonomous communication levels may further be determined and used with the risk levels to determine a total risk level associated with operation of the vehicle. The autonomous communication levels may indicate the types of communications, the levels of communication with other vehicles or infrastructure, or the frequency of autonomous communication. The total risk level may be used to determine or adjust aspects of an insurance policy associated with the vehicle.

An information-processing device includes a first feature-value information acquiring unit for acquiring an acoustic feature-value vector and a language feature-value vector extracted from a user's spoken voice. The information-processing device includes a second feature-value information acquiring unit for acquiring an image feature-value vector extracted from the user's facial image. The information-processing device includes an emotion estimating unit including a learned model including: a first attention layer using, as inputs, a first vector generated from the acoustic feature-value vector and a second vector generated from the image feature-value vector; and a second attention layer using, as an input, an output vector from the first attention layer and a third vector generated from the language feature-value vector, wherein the emotion estimating unit is for estimating the user's emotion based on the output vector from the second attention layer.

A vehicle is provided that comprises two or more radio frequency (RF) antennas and two or more RF transceivers to communicate wirelessly sensitive information associated with a user of the vehicle (the two or more RF antennas being at different physical locations on an exterior of the vehicle). The vehicle determines which one of the two or more RF antennas is receiving a strongest signal from a common signal source, selects a first RF transceiver associated with the RF antenna with the strongest signal to send the sensitive information associated with the user to the common signal source, and sends the sensitive information associated with the user to the first RF transceiver for transmission to the common signal source.

The present technique relates to an information processing apparatus, an information processing method, a program, and a moving body that can appropriately display content on top of a scene viewed by a user.

An aspect of the present technique provides an information processing apparatus that sets a frame as a superimposition location of content in a region corresponding to a surface of an object on the basis of a movement state of a user and generates visual information for displaying the content in the region corresponding to the set frame. The present technique can be applied to an apparatus that performs AR display of content.

The present technique relates to an information processing apparatus, an information processing method, a program, and a moving body that can appropriately display content on top of a scene viewed by a user.

An aspect of the present technique provides an information processing apparatus that sets a frame as a superimposition location of content in a region corresponding to a surface of an object on the basis of a movement state of a user and generates visual information for displaying the content in the region corresponding to the set frame. The present technique can be applied to an apparatus that performs AR display of content.

A station device in a biometric pre-identification system uses identity to perform one or more actions. Identities are determined (such as via a backend) using biometric information. A biometric pre-identification device obtains biometric information and/or a digital representation thereof from a person approaching the station device. The biometric pre-identification device transmits such to the station device, facilitating the station to begin and/or perform various actions. The station device begins or performs the actions using the identity determined based on the biometric information before the person arrives at the station device.

A processor-implemented vehicle controlling method includes: determining whether an object in a vehicle is a living object based on radio detection and ranging (radar) information received from a radar sensor; in response to a determination that the object is a living object, determining bioinformation of the object based on the radar information; and adjusting a temperature in the vehicle based on the bioinformation and temperature information received from a temperature sensor.

Computer implemented method for detecting a type of seat occupancy, comprising capturing, by means of an imaging device, an image of a seat, the image comprising depth data and intensity data, performing, by means of a processor device, a classifier algorithm on the captured image to determine a level of occupancy, wherein, if the determination indicates that the level of occupancy is above a predetermined threshold, the method comprises processing, by means of the processor device, the depth data with a convolutional neural network, to determine a type of occupation and wherein, if the determination indicates that the level of occupancy is below a predetermined threshold, the method comprises processing, by means of the processor device, the intensity data with a convolutional neural network to determine a type of occupation.