A seat adjustment device includes: an authentication unit that is configured to identify plural occupants of a vehicle; an acquisition unit that is configured to acquire information relating to each one of the plural vehicle occupants identified by the authentication unit; a determination unit that is configured to determine relationships between the plural vehicle occupants based on information acquired by the acquisition unit; and a control unit that is configured to control at least one of a position or an orientation of seats mounted in the vehicle based on determination results from the determination unit.

Provided is a seat in which a physical quantity can be controlled according to a physique of an occupant. One aspect of the present disclosure provides a seat including a seat body that supports a body of an occupant, at least one driving device that changes a physical quantity of the seat body, and a controller that controls the at least one driving device. The controller includes a model generator that generates a human body model of the occupant, an analyzer that analyzes a state quantity of the human body model, and an instructing device that drives the at least one driving device based on a result of an analysis by the analyzer.

A vehicle that can be customized and personalized via a mobile user profile. The vehicle can include a body, a powertrain, vehicle electronics, and a computing system. The computing system of the vehicle can be configured to: receive data fields of a driver profile of a user from a mobile device; estimate, using machine learning, configurations of vehicle functions for the vehicle according to the data fields; and control settings of a set of components of the vehicle, via the vehicle electronics, according to the estimated configurations.

Systems, Methods and Apparatuses are provided for a seat adjustment system for a vehicle which includes: a plurality of sensors configured to produce sensor data associated with an occupancy by an internal or external user of a particular seat of the vehicle and a processor, configured to receive the sensor data to: determine a state of one or more settings of the seat adjustment system for the particular seat of the vehicle based on stored data or the received sensor data; determine in a near future whether a particular seat will be occupied by the external user based on the received sensor data; determine currently whether a particular seat is occupied by the internal user based on the received sensor data; and adjust settings of the particular seat in accordance with determinations of the state of settings and occupancy of the particular seat.

Techniques are described for component configuration based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing the use of component(s) of the system by individual(s). The sensor data is analyzed (e.g., in real time) to determine an updated configuration for component(s) (e.g., an adjustment to the seat back, lumbar support of a car seat, etc.). The updated configuration may be communicated to the individual as a recommended configuration. In some implementations, the updated configuration may be communicated directly to the component which, on receiving and processing the configuration update, sends signals to various actuators to move the subcomponents of the component into the updated configuration. In some implementations, the sensor data is used to train, through machine learning, a model that provides configuration update(s) for component(s) based on the input sensor data.

Techniques are described for component design based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing use of one or more components of the system by one or more individuals. The sensor data from various instances of the component may be aggregated and analyzed to determine update(s) to the design of the component. For example, the aggregate sensor data may be analyzed to identify portions of the component frequently associated with movements by users (e.g., fidgeting, adjustments). The identified portion(s) can be presented graphically in a design view used to specify design modification(s) for the component. In some implementations, the aggregate sensor data is provided as input to a model that is trained, using machine learning, to output design modification(s) for the component based on the input aggregate sensor data.

Techniques are disclosed for systems and methods to detect and/or classify a vehicle occupant, such as a passenger seated within the cockpit of a vehicle. An occupant classification system includes an occupant weight sensor, an occupant presence sensor, and a logic device configured to communicate with the occupant weight sensor and the occupant presence sensor. The logic device is configured to receive occupant weight sensor signals from the occupant weight sensor and occupant presence sensor signals from the occupant presence sensor, determine an estimated occupant weight and an occupant presence response based, at least in part, on the occupant weight sensor signals and the occupant presence sensor signals, and determine an occupant classification status corresponding to the passenger seat based, at least in part, on the estimated occupant weight and/or the occupant presence response.

Techniques are disclosed for systems and methods to detect and/or classify a vehicle occupant, such as a passenger seated within the cockpit of a vehicle. An occupant classification system includes an occupant weight sensor, an occupant presence sensor, and a logic device configured to communicate with the occupant sensors. The occupant weight sensor includes at least first and second conductive electrodes separated by a dielectric layer, top and bottom protective plastic layers configured to support the respective first and second conductive electrodes, and adhesive layers disposed between the plastic layers and the conductive electrodes and between the conductive electrodes and the dielectric layer. The top protective plastic layer is longer and/or wider than the first conductive electrode and the bottom protective plastic layer is longer and/or wider than the second conductive electrode to provide edge protection against electrical shorts for the first and second conductive electrodes.

Various examples of tilting a human support surface in a vehicle are disclosed. A frame is attached to the surface of the vehicle. The vehicle also includes rotation system mounted between the frame and a vehicle surface. The rotation system can be controlled to tilt a human support surface of the frame towards a direction of acceleration associated with a driving maneuver as the driving maneuver is executed.

An electrically adjustable seating device includes at least one sensor element, a processor, a memory unit and at least one electromechanical actuator. The at least one sensor element is designed to detect predetermined seating parameter data relating to a person sitting on the seating device and to transmit the data to the processor. The processor is designed to determine person parameter data, relating to the person, from the seating parameter data, to extract control data, which are associated with the person parameter data, from the memory unit and to control the at least one electromechanical actuator on the basis of the control data.