A method of controlling switching to a manual driving mode in an autonomous vehicle provided with a foldable pedal device is provided. In the method, when a signal for switching a driving mode from an autonomous driving mode to the manual driving mode is generated in an autonomous vehicle provided with a foldable accelerator pedal device and a foldable brake pedal device, whether it is possible to switch the driving mode to the manual driving mode is determined by checking safety conditions; when the safety conditions are satisfied, it is determined whether a pop-up position of a pad of the foldable accelerator pedal device and a pop-up position of a pad of the foldable brake pedal device are normal pop-up positions, respectively; and only when the positions are the respective normal pop-up positions, the driving mode is switched to the manual driving mode.

A method for signaling a measured and/or a predetermined vital parameter and/or vital parameter pattern for a vehicle occupant in a vehicle involves arranging a functional element set up to have a visual and/or audible and/or haptic effect on at least one vehicle occupant, and a measured and/or a predetermined vital parameter and/or vital parameter pattern is signaled non-verbally by the functional element.

Systems, methods, and computer-readable media are disclosed for optimized driver seat and pedal positioning using ulna length. An example method may include determining, at a first time and using a sensor of a vehicle, an ulna length of a vehicle user. The example method may also include automatically adjusting a seating position of the vehicle user to a first seating position based on the ulna length of the vehicle user.

A system and method for classifying a type of interaction between a human user and a mobile communication device within a defined volume, based on multiple sensors. The method may include: determining a position of the mobile communication device relative to a frame of reference of the defined volume, based on: angle of arrival, time of flight, or received intensity of radio frequency (RF) signals transmitted by the mobile communication device and received by a phone location unit located within the defined volume configured to wirelessly communicate with the mobile communication device; obtaining at least one sensor measurement related to the mobile communication device from various non-RF sensors; repeating the obtaining, to yield a time series of sensor readings; and using a computer processor to classify the type of interaction into one of many predefined types of interactions, based on the position and the time series of sensor readings.

An embodiment method of controlling a moving object includes checking profile information of a user who rides in the moving object or status information of the user, checking a degree of risk based on the profile information or the status information of the user, setting an operation mode of the moving object based on the degree of risk, and controlling movement of the moving object based on the operation mode.

An autonomous vehicle includes a control system for remotely controlling the same. The autonomous vehicle includes an autonomous driving control apparatus having a processor that is configured to transmit an emergency request message to a control system when an emergency occurs to an emergency patient in the vehicle during autonomous driving, and the autonomous driving control apparatus is configured to direct the autonomous vehicle to follow a path with a shortest estimated required time among a contact path with an ambulance, a contact path with a neighboring vehicle capable of first aid, or a travel path to a hospital depending on remote control of the control system.

A vehicle driving support apparatus includes a forward environment recognizing device configured to recognize a traveling environment forward of a vehicle, a control device configured to perform adaptive cruise control and active lane keep centering control based on the recognized traveling environment, an electric power steering device configured to control a turning angle of wheels in a ganged manner in accordance with a steering angle received from a steering handle, and a driver monitoring system configured to detect changes in biological information of a driver who drives the vehicle. When the driver monitoring system detects a drop in alertness of the driver, the control device is configured to perform the adaptive cruise control and the active lane keep centering control and execute a steering handle idle mode that stops the electric power steering device from controlling the turning angle in the ganged manner in accordance with the steering angle.

A method performed by an electronic device is described. The method includes obtaining sensor data corresponding to multiple occupants from an interior of a vehicle. The method also includes obtaining, by a processor, at least one occupant status for at least one of the occupants based on a first portion of the sensor data. The method further includes identifying, by the processor, at least one vehicle operation in response to the at least one occupant status. The method additionally includes determining, by the processor, based at least on a second portion of the sensor data, whether to perform the at least one vehicle operation. The method also includes performing the at least one vehicle operation in a case that it is determined to perform the at least one vehicle operation.

Techniques for machine-trained analysis for multimodal machine learning vehicle manipulation are described. A computing device captures a plurality of information channels, wherein the plurality of information channels includes contemporaneous audio information and video information from an individual. A multilayered convolutional computing system learns trained weights using the audio information and the video information from the plurality of information channels. The trained weights cover both the audio information and the video information and are trained simultaneously. The learning facilitates cognitive state analysis of the audio information and the video information. A computing device within a vehicle captures further information and analyzes the further information using trained weights. The further information that is analyzed enables vehicle manipulation. The further information can include only video data or only audio data. The further information can include a cognitive state metric.

In accordance with one embodiment of the present disclosure, a method includes generating a plurality of icons, wherein each icon has a target frequency unique from each other, receiving brain activity data based on an epoch, generating a reference signal based on the epoch, calculating correlation coefficients between the brain activity data and the reference signal, wherein the correlation coefficients are calculated in a window that is within ±0.5 Hz of the target frequencies, including endpoints, determining a confidence score based on the correlation coefficients and the epoch, and determining a selected icon among the plurality of icons based on the correlation coefficients in response to the confidence score surpassing a threshold confidence score.