Embodiments of the present disclosure sets forth a computer-implemented method comprising receiving, from at least one sensor, sensor data associated with an environment, computing, based on the sensor data, a cognitive load associated with a user within the environment, computing, based on the sensor data, an affective load associated with an emotional state of the user, determining, based on both the cognitive load at the affective load, an affective-cognitive load, determining, based on the affective-cognitive load, a user readiness state associated with the user, and causing one or more actions to occur based on the user readiness state.

A system and a method for providing visual assistance to an individual suffering from motion sickness. The system includes: a sensor configured to measure movement data of a vehicle in real time; an artificial horizon device designed to generate an image of an artificial horizon in real time on the basis of the movement data of the vehicle; and a wearable augmented reality device designed to display the image of the artificial horizon in real time to an individual who is wearing the wearable device and is a passenger in the vehicle.

A vehicle and control method thereof are intended to promote safe driving of a driver by securing a map of surroundings around the vehicle and displaying a guide line for safe driving when the driver needs to display a guide line for safe driving while the vehicle is driving. The control method of the vehicle includes: checking whether a preset condition for generating a map and displaying a guide line is satisfied while the vehicle is driving; and generating a new map of the surroundings around a place where the vehicle is located and displaying the guide line for safe driving on the map when the preset condition for generating the map and displaying the guide line is satisfied.

The invention relates to a system for testing a driver assistance system of a vehicle, where the driver assistance system has at least one interior sensor and is designed to process sensor signals of the at least one interior sensor for monitoring a driver of the vehicle, the system comprising: simulation means for simulating at least one physical property of the driver which characterizes a physiological condition of the driver, in particular the driver's attentiveness, activity, fatigue, mood, state of health, and/or drug influence, and is able to be detected by the at least one interior sensor such that it can generate sensor signals as a function of the at least one simulated physical property; and an interface which interacts with the driver assistance system such that sensor signals are provided the driver assistance system as a function of the at least one simulated physical property. The invention further relates to a corresponding method.

An apparatus includes processing circuitry configured to determine an actual driving performance of a driver in a manual driving mode and a projected driving performance if the vehicle had been operated in an autonomous driving mode, compare the driving performance in the manual driving mode and the autonomous driving mode, and transmit a feedback to the driver based on the comparison. The processing circuitry can be further configured to determine a driver state of the driver in the manual driving mode, determine environmental driving condition of the vehicle, and establish a baseline behavior of the driver as a function of the driver state and the environmental driving condition.

In an autonomous driving method, a health physiological data range is added to an operational design domain (ODD) deployed on an autonomous driving system (ADS) as an applicable range of the ODD. The ADS receives real-time physiological data of a driver/passenger collected by a monitoring device. When a difference between the real-time physiological data and a health physiological data range is greater than a preset value, and a duration in which the real-time physiological data deviates from the health physiological data range is greater than a first preset duration, the ADS degrades an autonomous driving service being executed by an autonomous driving vehicle, and executing a first driving policy based on the difference and the duration.

A method evaluates a degree of fatigue of a vehicle occupant in a vehicle. A number of first fatigue indicators is provided which are determined according to computation rules from a plurality of first sensor values and each represent a degree of fatigue of the vehicle occupant. The first sensor values represent measured values of the vehicle and/or measured values relating to a current journey. A first metadata record is associated with each of the number of first fatigue indicators, wherein the first metadata records represent information about the characteristics of the sensors. The first sensor values are processed in the respective first fatigue indicators. A number of second fatigue indicators is provided which are determined according to computation rules from one or more second sensor values and each represent a degree of fatigue of the vehicle occupant. The second sensor values represent physiological and/or physical parameters of the vehicle occupants. A second metadata record is associated with each of the number of second fatigue indicators. The second metadata records represent information about the characteristics of the sensors. The second sensor values are processed in the respective second fatigue indicators. An overall fatigue indicator is determined which represents the degree of fatigue of the vehicle occupant by weighting the number of first fatigue indicators and the number of second fatigue indicators. The fatigue indicators are weighted according to the information about the characteristics of the sensors contained in the first metadata record and the second metadata record.

Example vehicle security systems and methods are described. In one implementation, a method receives a destination and a driving route to the destination for an autonomous vehicle to follow. A vehicle security system identifies a passkey associated with the driving route and communicates the passkey to a user designated to meet the autonomous vehicle at the destination. The vehicle security system confirms that the user designated to meet the autonomous vehicle possesses the passkey.

A computer generates an accident risk definition model to estimate a probability of hazard occurrence as an accident risk by inputting first in-vehicle sensor data collected in the past and hazard occurrence data having information on hazard occurrence from the first in-vehicle sensor data preset therein, generates accident risk estimation data by inputting second in-vehicle sensor data collected in the past to the accident risk definition model and estimating the probability of the hazard occurrence, generates an accident risk prediction model to predict the accident risk after a predetermined time by inputting first biological index data corresponding to the second in-vehicle sensor data and the accident risk estimation data, calculates second biological index data from second biological sensor data by acquiring the second biological sensor data of a driver, and predicts the accident risk after the predetermined time by inputting second biological index data to the accident risk prediction model.

Included are: a captured image acquiring unit to acquire a captured image obtained by imaging an occupant; a temperature image acquiring unit to acquire a temperature image indicating a temperature of a surface of a body of the occupant measured in a non-contact manner; a motion detection unit to detect a motion of the occupant on the basis of the captured image; a temperature detection unit to detect a temperature of a hand of the occupant on the basis of the temperature image; and an awakening level estimating unit to estimate an awakening level of the occupant on the basis of the motion of the occupant detected by the motion detection unit and the temperature of the hand of the occupant detected by the temperature detection unit.