A method for changing a control authority of an autonomous vehicle in consideration of an external environment includes determining a first risk level of a physical condition of a driver who drives the autonomous vehicle, determining a second risk level in response to one of a mental condition or a conscious condition of the driver, determining a driver proficiency level of a driver, and allocating a control authority of the autonomous vehicle to the driver or to the autonomous vehicle according to a result of a determination of the first risk level, the second risk level, and the driver proficiency level.

A vehicle includes a controller and a display. The display is mounted in the vehicle to surround a user in the vehicle. The controller is configured to determine travel information to be proposed to the user based on attribute information for the user, and present the determined travel information on the display.

A vehicle includes a processing device that detects an authentication device approaching the vehicle, determines a user's impaired state based on user condition information acquired using at least one device mounted on the vehicle, and performs a safe driving assistance service. The safe driving assistance service can prevent an impaired user from operating the vehicle.

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.

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 travel information sensor senses travel information of a host-vehicle. A biological information sensor senses biological information of a driver. A camera unit senses a facial expression of the driver. A communication unit acquires an agitating degree indicating a degree to which an other-vehicle agitates the driver of the host-vehicle, via a network. An agitated degree calculation unit calculates an agitated degree indicating a degree to which the driver of the host-vehicle is agitated by the other-vehicle. A danger degree determination unit determines a danger degree including whether the driver of the host-vehicle is agitated by the other-vehicle, based on the agitated degree and the agitating degree. A presentation unit warns the host-vehicle of the danger degree if it is determined that the driver of the host-vehicle is agitated by the other-vehicle.

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.