A method for determining the intention of a user of a vehicle to brake or accelerate, comprising: acquiring (100) a plurality of EEG signals on the user, applying (101) a predetermined spatial filter on the plurality of EEG signals so as to obtain a target EEG component, detecting (102) a spectral pattern in the EEG component corresponding to an intention to brake or detecting a phase pattern in the EEG component corresponding to an intention to accelerate.

A method is disclosed for operating a motor vehicle, wherein a control device determines a first index value from vital physiological data received from a sensor device at a first point in time. The control device generates a first actuator signal describing the activation of first actuator function, to be carried out by a first actuator that is selected based on the first index value. At a second point in time, the sensor device records additional vital physiological data and identifies a second index value. Both index values, each describing a physiological state of the user, are compared, and the result is used to determine whether the second index value meets an efficacy criterion that describes a predetermined degree of improvement in the physiological state brought about by the activated actuator function. If the second index value meets the efficacy criterion, the first actuator function is activated as soon as an index value is identified that describes the same physiological state.

Techniques for cognitive analysis for directed control transfer with autonomous vehicles are described. In-vehicle sensors are used to collect cognitive state data for an individual within a vehicle which has an autonomous mode of operation. The cognitive state data includes infrared, facial, audio, or biosensor data. One or more processors analyze the cognitive state data collected from the individual to produce cognitive state information. The cognitive state information includes a subset or summary of cognitive state data, or an analysis of the cognitive state data. The individual is scored based on the cognitive state information to produce a cognitive scoring metric. A state of operation is determined for the vehicle. A condition of the individual is evaluated based on the cognitive scoring metric. Control is transferred between the vehicle and the individual based on the state of operation of the vehicle and the condition of the individual.

Systems and methods for generating sound elements in a vehicle are present. In one example, a method comprises selecting a sound element, the sound element corresponding to a natural environment; and broadcasting the sound element via one or more speakers of a vehicle. In this way, a sound environment may be provided to a vehicle user based on the at least one vehicle state.

The present teaching relates to method, system, and medium, for switching a mode of a vehicle. Real-time data related to the vehicle are received, which include intrinsic/extrinsic capability parameters, based on which a set of tasks to switch from a current mode to a different mode is determined. A first duration of time required for the switch is determined based on a first risk evaluated with respect to the current mode and the real-time data. A task duration time needed by a driver to complete the task is estimated for each of the set of tasks. A second risk for the switching is estimated based on the required first duration of time and a total task duration times needed to complete the set of tasks. The switch is carried out when the second risk satisfying a criterion.

A method includes collecting sensor data from a sensor associated with a vehicle, storing the sensor data in a buffer associated with the sensor, wherein the buffer stores an amount of buffer data, analyzing the sensor data for a proximate event trigger. When the proximate event trigger is not detected, the method includes purging a portion of the sensor data exceeding the amount of buffer data. When the proximate event trigger is detected, the method includes stopping the purging of any of the sensor data and storing the sensor data of the buffer and the sensor data associated with the proximate event trigger and sending the sensor data of the buffer and the sensor data associated with the proximate event trigger to a server.

The disclosure includes embodiments for identifying an origin of abnormal driving behavior for improved vehicle operation. A method includes identifying an abnormal driving behavior of a driver of a vehicle at a time T. The method includes identifying a set of events that occurred within a predetermined time Δt before time T. The method includes executing, by a processor, a cause-and-effect analysis on the set of events to determine one or more events from the set of events that caused the abnormal driving behavior. The method includes executing a strategy to reduce the abnormal driving behavior so that vehicle operation is improved.

Methods, vehicles, and systems described herein generate alerts based on a generated macro state level indicative of a traffic hazard risk in view of a set of parameters which may lead driver to make various sub-conscious decisions. The set of parameters can include at least one of a vehicle parameter, driver state parameter, or external parameter.

An arithmetic device includes a first arithmetic chip configured to perform arithmetic processing for realizing driving assistance functions of a vehicle, and a second arithmetic chip configured to perform arithmetic processing for realizing the driving assistance functions of the vehicle, wherein, among the driving assistance functions, second functions realized by the first arithmetic chip along with the second arithmetic chip includes a function of providing at least some of first functions realized without depending on arithmetic processing of the second arithmetic chip in a state in which a task imposed on a driver has been reduced, as compared to the first functions.

An approach for assisting users with disabilities in an emergency situation relating to a vehicle is disclosed. The approach determines the profile of the passenger in a vehicle by noting preferences and disabilities associated with the passenger. After a vehicle accident, the approach determines the condition of the vehicle and the condition of the passenger. Based on various information received, the approach creates an action list of solutions for the passenger, wherein the action list has assigned dynamic risk scores. The approach determines the best solution based on the risk scores and selects the best solution from the action list.