A system for transportation includes a vehicle having at least one rider located in the vehicle and a data processing system for taking data from a plurality of social data sources. A hybrid neural network is connected to the data processing system. The system for transportation is to optimize satisfaction of the at least one rider based on processing the data from the plurality of social data sources with the hybrid neural network.

A transportation system includes an artificial intelligence (AI) system for processing a sensory input from a wearable device in a self-driving vehicle to determine an emotional state of a rider and optimizing a vehicle operating parameter to improve the rider emotional state. The AI system includes a recurrent neural network to indicate a change in the emotional state of the rider by a recognition of patterns of emotional state indicative wearable sensor data from a set of wearable sensors worn by the rider. The patterns are indicative of a first degree of a favorable emotional state of the rider and/or a second degree of an unfavorable emotional state of the rider. The AI system further includes a radial basis function neural network to optimize, for achieving a target emotional state of the rider, the vehicle operating parameter in response to the indication of the change in the rider emotional state.

A transportation system includes an artificial intelligence system for processing a sensory input from a wearable device in a self-driving vehicle to determine an emotional state of a rider and optimizing a vehicle operating parameter to improve the rider emotional state. The artificial intelligence system detects the rider emotional state in the self-driving vehicle by recognition of patterns of emotional state indicative data from a set of wearable sensors worn by the rider. The patterns are indicative of at least one of a favorable emotional state and an unfavorable emotional state of the rider. The artificial intelligence system is to optimize, for achieving at least one of maintaining a detected favorable emotional state of the rider and achieving a favorable emotional state of a rider subsequent to a detection of an unfavorable emotional state, the operating parameter of the vehicle in response to the detected emotional state of the rider.

A method of robotic process automation for achieving an optimized margin of vehicle operational safety includes tracking expert vehicle control human interactions with a vehicle control-facilitating interface, and recording the tracked expert vehicle control human interactions in a robotic process automation system training data structure. The method further includes tracking vehicle operational state information of a vehicle, and recording vehicle operational state information in the robotic process automation system training data structure. The method further includes training, via at least one neural network, the vehicle to operate with an optimized margin of vehicle operational safety in a manner consistent with the expert vehicle control human interactions based on the expert vehicle control human interactions and the vehicle operational state information in the robotic process automation system training data structure, and controlling at least one aspect of the vehicle with the trained artificial intelligence system.

A driver state determination apparatus detects decline of the physical function of a driver and confirms an abnormal state before the driver becomes unable to drive. A vibration apparatus vibrates a steering wheel; a vibration detector detects vibration of the steering wheel; a steering angle sensor detects a steering angle of the steering wheel; and a controller controls the vibration apparatus. The controller provides vibration at a predetermined excitation frequency to the steering wheel by the vibration apparatus, calculates a steering torque level at the excitation frequency based on the vibration detected by the vibration detector, and determines that the driver is in an abnormal state when a correlation coefficient between a time variation of a steering torque level while the vibration is being provided and a time variation of a steering angle while the vibration is being provided is equal to or more than a predetermined value.

The present application relates to a control stick for a military aircraft, a system for managing health information regarding a pilot, a military aircraft, and a simulator for a military aircraft. According to the control stick for a military aircraft, the system for managing health information regarding a pilot, the military aircraft, the military aircraft, and the simulator for a military aircraft of the present application, health information regarding a pilot may be continuously monitored, and flight accidents such as personal damage and material damage may be prevented via the collection of the health information.

Chionophobia intervention systems and methods are disclosed herein. An example method includes detecting snowfall from a vehicle sensor of a vehicle or a service provider, displaying a prompt on a human machine interface (HMI) to query a user regarding additional assistance in response to the detection of the snowfall, activating a first stage response after detecting the snowfall, determining when the first stage response is insufficient based on feedback received from the user, and activating a second stage response when the feedback received from the user indicates that the first stage response is insufficient.

A biological information measuring apparatus according to an embodiment includes a calculating unit and a measuring unit. The calculating unit is configured to calculate information related to a pulse wave of a subject, on the basis of luminance information of a picture signal of the subject. The measuring unit is configured to measure a fluctuation of blood pressure of the subject based on an increase or a decrease in the information related to the pulse wave, in accordance with a site of the subject from which the picture signal was obtained.

An inattentive state determination device includes: a drowsiness predictor that derives drowsiness prediction data for predicting an occurrence of drowsiness in a person; a drowsiness and inattentiveness deriver that derives a degree of drowsiness and inattentiveness of the person; and an inattentiveness determiner that determines whether the person is in an inattentive state, based on the drowsiness prediction data and a current degree of drowsiness and inattentiveness.

Technologies for providing a cognitive capacity test for autonomous driving include a compute device. The compute device includes circuitry that is configured to display content to a user, prompt a message to the user to turn user's attention to another activity that needs situational awareness, receive a user response, and analyze the user response to determine an accuracy of the user response and a response time, wherein the accuracy and response time are indicative of a cognitive capacity of the user to assume control of an autonomous vehicle when the autonomous vehicle encounters a situation that the vehicle is unable to navigate.