A train monitoring system in the present embodiment is provided with a compartment camera 10 that takes an image of the inside of a compartment of the train, a processing server 30 that calculates a vacancy probability for each reserved seat on the basis of the image taken by the compartment camera 10 while the train is running and detects a resale candidate seat by comparing the vacancy probability with a threshold value, and a touch panel monitor 40 that displays resale candidate seat information indicating the resale candidate seat that has been detected by the processing server 30.

According to the present invention, a physique determination apparatus includes an image acquisition unit configured to acquire an image of an occupant, a facial feature point estimation unit configured to estimate facial feature points of the occupant based on the image, a head position estimation unit configured to estimate a relative position of a head of the occupant with respect to the camera based on the facial feature points, a body part estimation unit configured to estimate sizes of a plurality of parts of the occupant in the image, a skeleton estimation unit configured to estimate a skeleton size indicating an actual size of each part of the occupant based on the relative position and a size of each of the parts, and a physical determination unit configured to determine a physique of the occupant based on the skeleton size of each part of the occupant.

In an analysis processing device for performing analysis on body displacements of passengers in a vehicle, a calculation unit is configured to calculate seated and standing positions of the passengers based on recognition data. A period determination unit is configured to determine an analysis period of the vehicle according to a function that changes the analysis period depending on the seated and standing positions of the passengers.

An amusement park attraction system includes a plurality of autonomous vehicles configured to accommodate one or more guests and located in an attraction dispatch area. An individual autonomous vehicle includes a vehicle controller including a memory storing instructions and a processor configured to execute the instructions, the instructions configured to cause the vehicle controller to receive an indication that the individual autonomous vehicle is loaded at capacity within the dispatch area and drive autonomous operation of the individual autonomous vehicle from the dispatch area to enter the ride path.

An information processing apparatus includes a receiving unit that receives authentication information for authenticating a passenger in a vehicle, an authentication unit that authenticates a passenger based on the authentication information, an assessment unit that assesses whether the authenticated passenger is standing up, a measuring unit that measures a period of time during which it is assessed that the authenticated passenger is standing up as a period of time during which the passenger is actually standing up, a calculation unit that calculates points or digital currency used in a reward program such that the authenticated passenger who stands up for a longer period of time gets more points or a higher amount of digital currency, and a transmission unit that transmits the calculated points or digital currency to a storage unit. The storage unit is configured to store the points or the digital currency in association with the passenger.

An information processing apparatus capable of controlling a mobile object on the basis of an instruction by an utterance of a user identifies which scene a use scene of a target user is among a plurality of use scenes in a case where the mobile object is used, acquires utterance information of the target user, and selects a different machine learning model according to the identified use scene of the target user. The information processing apparatus estimates an intent of an utterance of the target user by using the selected machine learning model.

An occupant state detection system includes an occupant monitoring apparatus, an occupant state detection apparatus, and a determination apparatus. The occupant state detection apparatus includes one or more processors and one or more memories. The one or more processors cooperate with one or more programs included in the one or more memories to receive occupant monitoring data from the occupant monitoring apparatus and determination data from the determination apparatus. On the basis of the determination data, the one or more processors make assignment of a part to be monitored, of an occupant in a vehicle, to be monitored by the occupant monitoring apparatus, to detect a physical state of the occupant.

Aspects of the present disclosure include methods, systems, and non-transitory computer readable media for receiving an initial image of the area, receiving an updated image of the area, detecting at least one of a person in the area or a movement in the area based on the updated image, comparing the updated image and the initial image to identify a change in the area, and determining a presence or an absence of the occlusion based on the change in the area and the at least one of the person or the movement in the area.

Operation of a head-worn display device in a motor vehicle includes a control device checking a condition of whether there is a vehicle occupant in the motor vehicle other than a driver, when the motor vehicle is not operated in an autonomous driving mode. If the condition is satisfied then a display of all content by the head-worn display device is stopped by the control device if the display device is virtual reality glasses and the display of exclusively traffic-relevant content by the display device is enabled by the control device if the display device is augmented reality glasses.

The present disclosure describes a method for occupancy class prediction, such as for occupancy class detection in a vehicle. In aspects, the method includes determining, for a plurality of points of time, measurement data related to an area and determining, for a plurality of points of time, occlusion values based on the measurement data. The method further includes selecting, for a present point of time, one of a plurality of modes for occupancy class prediction based on the occlusion values for at least one of the present point of time and a previous point of time and/or based on one of the plurality of modes for occupancy class prediction selected for the previous point of time. The method additionally includes determining, for the present point of time, one of a plurality of predetermined occupancy classes of the area based on the selected mode for the present point of time.