DE102019208956A1 - Control device for use in a motor vehicle - Google Patents

Abstract

A control device (10) for use in a motor vehicle (11) comprises: a first interface (12) to receive data from a first sensor (14), the data including a recorded vital sign (76) of a vehicle occupant (15) - an evaluation device (16) for evaluating (38) data received via the first interface (12), - a second interface (18) to send signals from the evaluation device (16), the recorded vital sign (76) by means of of the first sensor (14) is detected without physical contact with the vehicle occupant (15).

Description

The present invention relates to a control device for use in a motor vehicle, a use of the control device, a control method for use in a motor vehicle and a system for controlling a motor vehicle.

The inventors of the present invention have recognized that vehicle occupants of autonomously driving vehicles can perform a large number of other activities, for example reading or writing, instead of driving a vehicle. This can lead to more symptoms of motion sickness. The inventors have recognized that if a person's vestibular sensation does not match the visual sensation, this can lead to a lack of comfort and in particular to symptoms of motion sickness (kinetosis). Secondary activities of a vehicle occupant in the motor vehicle can result in the vehicle occupant not being able to perceive the surroundings as a whole and consequently not being able to assess the current movement of the vehicle and not be able to anticipate future movements of the motor vehicle. The inventors have recognized that a driving style and a design of the motor vehicle, for example the choice of a seat or an interior design, as well as an interaction of the motor vehicle with the vehicle occupants are decisive for the occurrence of symptoms of motion sickness in a vehicle occupant who performs secondary activities or not.

US 2017/0313326 A1 describes a sensory stimulation system for an autonomous vehicle. Depending on the maneuver, the stimulation system can determine a set of visual stimulations. Passengers of the autonomous vehicle can receive a visual display of the respective maneuver.

The new control device for use in a motor vehicle comprises a first interface in order to receive data from a first sensor. The data include a recorded vital sign of a vehicle occupant. The control device also includes an evaluation device for evaluating data received via the first interface. The control device also includes a second interface in order to send signals from the evaluation device. The recorded vital value is recorded by means of the first sensor without physical contact with the vehicle occupant.

The expression “without body contact” can be understood to mean that the first sensor does not touch the vehicle occupant and / or their clothing.

The evaluation device can be set up to carry out a self-learning algorithm. The control device can be set up to end the self-learning algorithm at the end of a training phase.

The control device can be an electronic control device, also known as an electric control unit, or ECU for short. In particular, the control device can be or have an ADAS / AD domain ECU.

The control device can preferably have at least one processor. The processor can be an electronic circuit that controls other electronic circuits in accordance with transferred commands and thereby drives a process.

The control device can be set up, in particular programmed or programmed, in order to solve tasks with AI (artificial intelligence) algorithms. Artificial intelligence is a generic term for the automation of intelligent behavior. The control device can be set up, in particular programmed or programmed, to have an artificial neural network. An artificial neural network, called Artificial Neural Network in English, is an intelligent algorithm that can be designed according to a biological model. The control device can be set up, in particular programmed or programmed, in order to execute an intelligent algorithm. For example, an intelligent algorithm learns to react appropriately to new information (monitored learning).

The control device can for example have a housing, for example made of metal or plastic or made of an alloy, for example a combination of metal and plastic. For example, the control device can have a cooling system. This is particularly advantageous with a high computing power and a corresponding heat emission.

The control device can be set up, in particular programmed or programmable, in order to regulate longitudinal and / or lateral control of the vehicle, in particular by means of the signals.

The control unit can be designed to process large amounts of data in short times.

The motor vehicle can be a passenger car or a truck or a utility vehicle. The motor vehicle can in particular be an autonomously driving vehicle. For example, the vehicle can be a vehicle with a high level of automation, in which the The driver has less and less control of the vehicle and becomes a passenger. In addition to the “loss of control” phenomenon, automation also enables a vehicle occupant to perform secondary activities such as reading, working and / or sleeping in the motor vehicle.

An interface, in particular the first interface and / or the second interface and / or a third interface and / or a fourth interface, can be a component between at least two functional units. An exchange of logical variables, for example of data and / or electrical signals, can take place at the first interface and / or at the second interface, either only unidirectionally or bidirectionally. The exchange can be wireless or wired. An interface can exist, for example, between software and software, hardware and hardware, and software and hardware and hardware and software.

For example, the first interface and / or the second interface can be set up to conduct information wirelessly. The first interface and / or the second interface can be set up to receive and / or send data and / or signals in a contactless manner or by means of a line. The first interface and / or the second interface can be set up both to receive data and to send signals.

The second interface can be set up, for example, to route the signals to a vehicle system by means of electromagnetic waves and / or by means of a cable. The second interface can in particular be set up to control the vehicle control actuators with vehicle control commands.

The first interface and the second interface can be integrated in one component, for example. The component can be set up, in particular programmed or programmable, to receive data and / or to send signals, in particular to receive or send information.

The data can preferably be measurement data, in particular measurement data from the first sensor. The data can have at least one physical variable, for example. For example, the data can have pairs of measured values and the associated measurement times and / or an identifier of an associated vehicle occupant.

The vehicle occupant can be the vehicle driver and / or a passenger. The vehicle occupant can be a person, for example a child or an adult.

The evaluation device can be configured to process the data with KI algorithms in order to calculate the signals for the vehicle system. The vehicle system can include a vehicle control system.

An evaluation device can be a device that processes incoming information and outputs a result resulting from this processing. In particular, the evaluation device can have an electronic circuit, for example the above-mentioned one or more processors.

The evaluation can include data processing, for example performing a calculation, in particular performing an algorithm, for example the self-learning algorithm. The evaluation can include, for example, a comparison with other data and / or storage in a database and / or a graphic representation.

The signals can include, for example, requirements with regard to a vehicle speed and / or a vehicle acceleration and / or a change in a vehicle acceleration over time. The vehicle speed and / or the vehicle acceleration and / or the change in the vehicle acceleration over time can, for example, be three-dimensional vectors, for example in a Cartesian coordinate system. The signals can have requirements with regard to up to six degrees of freedom, for example with regard to three axes of a Cartesian coordinate system and / or with regard to three angles of rotation about the axes of the Cartesian coordinate system.

The terms "sending" and / or "receiving", in particular of data and / or signals, can include contactless transmission, for example by means of electromagnetic waves, for example by means of WLAN or Bluetooth, and / or transmission by means of a connection, for example a line, be understood.

The evaluation device can be set up to carry out one or more computing operations, in particular computing models. The calculation models can be selected from a group of models comprising an HFR model, a 6DOF-SVC model, multiple linear regression, a self-learning algorithm and a neural network. The models mentioned increase in complexity in the order in which they are mentioned and in the amount of deviations from, so be more precise. For example, the MSI (motion sickness index) can be calculated and / or validated from the models mentioned. Driving dynamics values can serve as input for the arithmetic operations.

For example, the evaluation device can be set up to generate a strategy using the self-learning algorithm from links between the data and associated driving maneuvers by means of a learning process. For example, the self-learning algorithm can be used to identify a pattern and / or a regularity in the data, in particular for associated driving maneuvers. Furthermore, the evaluation device can be set up to store these links from the data and the driving maneuvers and / or the pattern and / or the regularity. The self-learning algorithm can be set up, for example, to forecast unknown data after the training phase, in particular to carry out a transfer. The self-learning algorithm can be based on a symbolic approach or a non-symbolic approach, for example.

In particular, the self-learning algorithm can follow the principle of neural networks. The self-learning algorithm can be set up to carry out a classification and / or a regression and / or a prognosis. The self-learning algorithm can be set up to draw logical conclusions and / or to carry out a pattern analysis and / or to carry out a pattern recognition, for example face recognition and / or speech recognition. The self-learning algorithm can be set up to carry out a pattern prediction, for example for predicting a driving maneuver and / or an interaction with a vehicle occupant. The evaluation device can be set up, for example, to find and / or plan a suitable route, for example to a destination specified by the vehicle occupant. The evaluation device can be set up to train the self-learning algorithm with new data, for example continuously or only during the training phase. The evaluation device can furthermore be set up to carry out an approximation method. The self-learning algorithm can include, for example, a support vector method.

The self-learning algorithm can be, for example, a regression algorithm, in particular a regression algorithm with linear regression.

The self-learning algorithm can be set up to enable the control unit or the motor vehicle to learn a comfortable driving style, in particular for the vehicle occupant (s). This allows the data, for example affective-neurocognitive measurements of the vehicle occupants, to be used, in particular to detect that specific driving dynamics are causing symptoms of motion sickness. For example, an objective motion sickness index (MSI) can be recorded for this purpose.

The inventors have recognized, for example, a connection between accelerations and physiological changes in a galvanic skin conductivity due to a motion sickness triggered in autonomous driving. For example, a longitudinal dynamic and / or a combination of a longitudinal and a lateral dynamic can have a strong influence on the symptoms of motion sickness. The inventors recognized that there is a direct connection between an acceleration and an evoked phasic reaction of an electrodermal activity. Motion sickness can be triggered, for example, by playing a game on a tablet PC while driving the motor vehicle.

For example, a series of driving maneuvers can be carried out during the training phase, for example driving maneuvers with different frequencies and sequences, for example with different vehicle speeds and / or different periodic and / or non-periodic driving routes.

The data can contain, for example, a self-assessment of the vehicle occupant, for example a self-assessment with regard to a physical condition. Alternatively or in addition, the data can include an electrodermal response to acceleration. The driving maneuvers can, for example, have a longitudinal dynamic, a lateral dynamic, a vertical dynamic, a combination of a longitudinal and a vertical dynamic, a combination of a longitudinal and a lateral dynamic, a combination of a lateral and a vertical dynamic or a combination of a have longitudinal and lateral and vertical dynamics.

The data can have, for example, a mean deviation and / or a standard deviation, for example in the case of multiple measurements and / or in the case of repetitive maneuvers. The data can, for example, be normalized or normalized by means of the control device. The data can include, for example, multi-dimensional vectors, for example three-dimensional vectors, for example including a phasic response to a driving maneuver and / or a number of Reactions to a driving maneuver and / or a time. The data can for example have a matrix. For example, the number of reactions can be used to form a mean value which can be plotted against time. The data can, for example, have a single-sweep matrix, in particular a matrix representation of single-sweep signals. Single sweeps are individual reactions to a stimulation, for example to a driving maneuver. The control device can be set up to calculate mean values from the single-sweep matrix.

The inventors recognized that the phasic part of the electrodermal reaction takes place after one to six seconds, in particular as a reaction to a driving maneuver. In order to obtain a connection between accelerations and a phased electrodermal response, the control unit can be set up to normalize the single-sweep matrix. The inventors recognized that a standard deviation is lowest in a period of three to eight seconds, in particular four to seven seconds, in particular six seconds. The inventors recognized that signals, in particular phasic electrodermal responses, are related to longitudinal accelerations.

The inventors recognized a linear Pearson correlation between a subjective sickness motion level (SMSL) and an electroencephalogram (EEG).

For example, the control device can be set up to evaluate an EEG signal, in particular to determine an SMSL from an EEG signal. The control device can be set up, for example, to break down data, in particular including EEG signals, into different frequency bands, for example into a δ band. Furthermore, the control device can be set up to evaluate data, in particular including EEG signals, with regard to a phase amplitude coupling (PAC), wherein the PAC can be based on the principle of cross-frequency coupling.

The inventors have recognized that there is a positive correlation between the SMSL and an intensity of the δ band of an EEG signal, in particular an EEG signal in a region of the prefrontal cortex. The inventors also recognized that there is a high negative Pearson correlation between SMSL and PAC in the occipital region. For example, the intensity of the δ band and / or the PAC can be used to determine a motion sickness, in particular a severity of a motion sickness, in particular by means of the evaluation device.

During the training phase, the vehicle occupant can, for example, sit in a rear seat of the motor vehicle. The vehicle occupant can, for example, be instructed by the control device during the training phase to operate a tablet computer.

For example, the evaluation device can have a fourth interface. The second interface and / or the fourth interface can, for example, be or can be connected to a screen. For example, the data and / or the signals and / or instructions can be displayed to the vehicle occupants by means of the screen. For example, the data can include an assessment of the SMSL of the vehicle occupants. For example, the screen can be set up to send the data, in particular assessments of the SMSL, to the evaluation device and / or to receive and / or display them therefrom. For example, the SMSL can have a value on a scale from one to ten. The SMSL can, for example, be recorded and / or processed by means of the control device during different time intervals and / or for different vehicle occupants. The data can, for example, have a function of the SMSL versus time. The function can be a step function.

The first sensor can for example be an EEG measuring device, for example an EEG measuring device with 32 channels, in particular for a sampling rate of 55 Hz. For example, the evaluation device can be set up to subject the data to a bandpass filtering (power band analysis), for example between 1 Hz and 100 Hz, in particular between 1 Hz and 60 Hz, in particular between 1 Hz and 45 Hz (PAC). The evaluation device can, for example, be set up to use a zero phase FIR filter, for example based on a Hamming window, in particular for bandpass filtering.

The evaluation device can be set up to carry out a PAC algorithm, including, for example, the use of a G phase and / or a γ amplitude (high and low y, for example 30 to 45 Hz and 45 to 60 Hz), for example by means of the evaluation device calculated and plotted over time. For example, the evaluation device can be set up to examine and / or use a Pearson correlation between PAC and SMSL or the 5-band and SMSL for different electrode channels and / or different vehicle occupants. The inventors have recognized that there is a high negative correlation between the data, in particular PAC data, and the SMSL in an area of the back of the head. Furthermore, the inventors have recognized that there is a high positive correlation between SMSL and an intensity of the 5 band is present in an area of the prefrontal cortex. The evaluation device can be set up to infer a severity of motion sickness from a PAC analysis, in particular from an intensity of the 5-band of EEG data.

The inventors have recognized correlations between the nervous system and motion sickness.

The vehicle occupant can, for example, sit on a left front seat during the training phase and play games on a tablet PC. For example, the control device can be set up to request the vehicle occupant to transmit a degree of perceived motion sickness to the control device, for example by means of the tablet PC. For example, the control device can be configured to use the first interface to send values of motion sickness of the vehicle occupant from the tablet PC to the evaluation device.

The evaluation device can be set up, for example, to classify the vehicle occupant in one of several different categories, for example in one of three different categories, on the basis of a sensitivity to driving maneuvers. For example, the data can have an assessment of the vehicle occupant with regard to motion sickness for different times. Travel sickness can be classified by the vehicle occupant on a scale between 0 and 10, for example. The inventors recognized a high correlation between a GSR (electrodermal activity) and symptoms of motion sickness. Furthermore, the inventors recognized a correlation between a heart rate and symptoms of motion sickness. As rice sickness progresses, both an average heart rate of the vehicle occupant and an average coefficient of variation can increase. For example, the evaluation device can be set up to infer the severity of a motion sickness from galvanic skin resistance and / or from the heart rate.

The data can for example contain information about a condition of the motor vehicle. For example, the data can have information of the motor vehicle selected from a group comprising an acceleration in the x direction, an acceleration in the y direction, an acceleration in the z direction, a roll, a yaw, a pitch, a rotation about a Cartesian axis Coordinate system, information on a steering movement, information on a lifting movement, information on a rolling movement, information on a longitudinal movement, information on a lateral movement and information on a transverse displacement. For example, the evaluation device can be set up to use the mentioned information on the motor vehicle in the self-learning algorithm, in particular to extract features that can serve as training data. The evaluation device can be set up to select a region of interest (ROI) from the data and / or to extract different triggers for motion sickness. The triggers can be determined by the evaluation device using different threshold values, for example from a heart rate.

The evaluation device can be set up, for example by means of the self-learning algorithm, to predict and / or record a GSR curve in order to warn of motion sickness and / or to prevent motion sickness. The evaluation device can be set up to predict or prevent motion sickness by means of information on the driving dynamics of the motor vehicle.

The evaluation device can be set up to train the self-learning algorithm using different data. The data, in particular data acquired during the learning phase, can include GSR values and / or values relating to driving dynamics, for example measured driving dynamics or predetermined driving dynamics.

The evaluation device can in particular be set up to determine different statistical values from the data. For example, the evaluation device can be set up to determine extreme values, for example minima and / or maxima, and / or mean values and / or standard deviations from the data. The GSR values can have two different components, for example. The GSR values can have a tonic, in particular a slow component, and / or a phasic, in particular a fast component. The data can for example have phasic GSR values and / or tonic GSR values.

For example, the evaluation device can be set up to extract tonic GSR values after a trigger event, for example an acceleration and / or a rotation. For example, data on specific, in particular predefined, trigger events, in particular trigger events for motion sickness, can be recorded during the learning phase. For example, the evaluation device can be set up to determine such trigger events, for example by means of threshold values for driving dynamics and / or by means of threshold values of a white box model.

The evaluation device can be set up to take comfort parameters accordingly ISO2631-1 to be determined and / or used. The data can, for example, correspond to comfort parameters ISO2631-1 include. For example, the evaluation device can be set up to take a closer look at different time windows in order to carry out the self-learning algorithm.

The evaluation device can be designed, for example, to evaluate a learning success of the self-learning algorithm. The evaluation device can in particular be set up to compare recorded data and predicted data, in particular to evaluate a learning success of the self-learning algorithm and / or to determine a duration and / or an end of a learning phase.

The learning success can for example be represented graphically by means of the control device, for example by means of a color-coded representation, in particular by means of the screen. The color-coded representation can, for example, have a colored pixel for each learned driving situation. For example, a distance from the origin of the color-coded representation can indicate a direction of the driving dynamics, for example a left curve and / or a right curve or a braking and / or an acceleration. A color of the color-coded representation can, for example, provide information on the frequency of the learned driving situation.

As a result, the self-learning algorithm can be trained by means of the new control unit, in particular with regard to a vehicle occupant, that a first sensor, for example a portable sensor, only has to be worn during the learning phase in order to prevent travel sickness after the learning phase based on driving dynamics, in particular a current driving dynamics, predict and / or prevent. By increasing the number of data, the prediction and / or prevention of motion sickness can be improved by means of the self-learning algorithm.
The evaluation device can be set up to predict motion sickness by means of a model, in particular by means of the self-learning algorithm. For example, the control device can be set up to carry out bio-feedback. As an alternative to this, the control device can be set up to carry out the control method without bio-feedback. The evaluation device can be set up to use a white box model. Alternatively or additionally, the evaluation device can be set up to use a Black Bock model. The evaluation device can be set up to use a filter method, for example a filter method based on a “one-third octave”, for example in accordance with ISO2631-1.

For example, the evaluation device can be set up to use methods as described in EN12299. The evaluation device can be set up to use a “Human Factor Research Model” and / or a “6 Degrees of Freedom - Subjective Vertical Conflict Model (6DOF-SVC)”. For example, the severity of the motion sickness can be calculated using the methods mentioned. The evaluation device can be set up to predict the course of motion sickness by means of a linear regression.

The inventors recognized that a driving maneuver during a time window of ten seconds can have an impact on the occurrence of motion sickness. For example, the evaluation device can be set up to determine the condition of a vehicle occupant for the next 0 to 2000 seconds, in particular for the next 5 to 1000 seconds, in particular for the next 10 to 300 seconds, in particular for the next 1000 seconds or for the next 100 seconds or predict for the next 10 seconds.

The evaluation device can be set up to learn, by means of a relationship between driving dynamics, which can be detected by the first sensor, for example, and the MSI, to avoid a type of maneuver or a type of dynamics in the future and / or to take countermeasures if Driving dynamics cannot be prevented.

The evaluation device can be set up to generate the signal by means of an end-to-end calculation or by means of two parallel arithmetic operations. For example, the evaluation device can be set up to calculate the MSI from a first path from the data of the first sensor. Furthermore, the evaluation device can be set up to calculate driving dynamics from the MSI by means of a second path, in particular driving dynamics to prevent motion sickness. The first path can be configured parallel to the second path. In the case of an end-to-end calculation, the data from the first sensor can preferably be used to infer future driving dynamics directly.

For example, the evaluation device can be set up to carry out two self-learning algorithms, in particular two parallel self-learning algorithms. For example, the evaluation device can be set up to measure by means of a galvanic skin resistance and / or a heart rate and / or a temperature by means of two self-learning algorithms to generate the signals.

A first self-learning algorithm can be designed to determine the MSI. A second self-learning algorithm can be designed to determine driving dynamics, for example a matrix of driving dynamics. Using galvanic skin resistance and body temperature and / or heart rate can improve a prediction of the MSI. The first self-learning algorithm and / or the second self-learning algorithm can for example have a decision tree or a random forest. Here, for example, normalization can be dispensed with. The first self-learning algorithm and / or the second self-learning algorithm can, for example, use methods selected from a graybox method, a 6DOF SVC model, a method described in ISO2631-1 and a method described in EN12299.

The evaluation device can be set up to carry out the self-learning algorithm during a training phase. The evaluation device can be set up to carry out a pattern recognition using the signals during the training phase. The data can have a driving dynamics value, for example. In this way, individually coordinated driving maneuvers can be selected and / or a vital value estimation can be carried out specifically during a journey and a corresponding instantaneous reaction to a body reaction of a vehicle occupant can be made.

The training phase has a beginning and an end. The start of a training phase can, for example, be when an unknown vehicle occupant, for example a new driver, starts driving. At the end of the training phase, a learning process can be carried out using the self-learning algorithm. The control device can be set up to carry out the self-learning algorithm only during the training phase. The training phase can be a first time interval, for example. Alternatively, the training phase can be a first driving route. The first travel distance can be, for example, between 100 m and 2,000 km, preferably between 100 km and 1,000 km, in particular between 400 km and 600 km, for example 500 km.

The control device can be set up in such a way that data are recorded by the first sensor only during the training phase and / or data are only passed via the first interface to the evaluation device during the training phase. For example, the control device can be set up to record the recorded vital signs of a vehicle occupant continuously or in a clocked manner during the first time interval.

For example, the evaluation device can receive data from the first sensor during the first time interval, with no recorded vital sign of the vehicle occupant being recorded by the evaluation device during a time outside the first time interval. As a result, installation space in the vehicle, in particular in the passenger compartment, can be gained outside of the training phase, since the first sensor can be stowed away, for example. It is also advantageous here that a vehicle occupant does not have to wear the first sensor all the time, for example in a wired manner. As a result, driving comfort for the vehicle occupant can be significantly increased.

For example, the control device can be set up to detect a corresponding body reaction of a vehicle occupant in parallel to the driving dynamics that occur, for example a speed, an acceleration or a change in acceleration, in particular longitudinal, lateral or vertical. The training phase can be a predefined or determined period. The training phase can be defined as a function of an underlying phenomenon, for example a toxic hypothesis in the case of motion sickness.

The first sensor can be connected to the first interface by means of an electrical line, for example. The data can reach the evaluation device by means of the electrical line from the first sensor via the first interface. As an alternative to this, the data can reach the evaluation device from the first sensor via the first interface by means of electromagnetic waves.

The first sensor and / or the first interface and / or the second interface and / or the evaluation device and / or the vehicle system can, for example, be combined to form an evaluation and control device. As an alternative to this, the elements mentioned can be designed at least partially as separate components.

The vehicle system can be a controller, for example. For example, the vehicle system may include a vehicle control actuator. Vehicle control actuators can issue commands, preferably in the form of electrical signals. The commands can be converted into mechanical movement or other physical quantities, for example into a pressure, and can actively intervene in a process. The vehicle control system can be set up to regulate longitudinal and / or lateral guidance of the autonomous vehicle. For example, this can Vehicle system be an element of the vehicle, for example a steering and / or a transmission and / or an engine and / or lighting. The signals can for example contain commands, in particular for the implementation of an action plan, in particular for the vehicle system.

The system can for example have a second sensor. The second sensor can be, for example, a surroundings detection sensor. The surroundings detection sensor can be a sensor that can be arranged on a vehicle, in particular for detecting the surroundings of the vehicle. Environment detection sensors can be understood as vision components, control devices as thinking components and vehicle control actuators as action components of a vehicle control system.

The surroundings detection sensor can be designed for use in the automotive sector, that is to say it can be functional and shockproof over a wide temperature range, for example from −50 ° C. to + 50 ° C., and / or consume relatively little energy. Environment detection sensors can in particular include radar sensors, lidar sensors, camera sensors and / or sound sensors or a fusion of the aforementioned sensors.

The first sensor can for example comprise an optical sensor. The control device can be set up to carry out an optical method, in particular an optical method, in order to record the MSI or a history of travel sickness of the vehicle occupant. The optical sensor can be set up to receive and / or transmit electromagnetic radiation, for example light.

The first sensor can have a camera system. The camera system can have one or more elements selected from a group comprising a receiver for electromagnetic waves, a transmitter for electromagnetic waves, a camera, an NIR camera, a thermal imaging camera, a hybrid thermal imaging camera, a night vision device, a lighting device, a light amplifier, an optical filter, a housing and a motion detector. For example, one or more of the named elements can be arranged in a housing, for example a camera system housing.

The camera can for example be a video camera, for example for the continuous observation of one or more vehicle occupants. The camera can be set up to generate an optical image. The camera can be set up to generate an optical image using visible light, i.e. using electromagnetic waves in the wavelength range from 380 nm to 750 nm, and / or using red light, i.e. using electromagnetic waves in the wavelength range from 650 nm to 750 nm.

The NIR camera can be set up to generate images by means of electromagnetic waves in the wavelength range between 780 nm to 3 μm. The thermal imaging camera can be set up to generate an optical image by means of electromagnetic radiation with a wavelength between 0.7 μm and 1000 μm. The thermal imaging camera can be set up to detect a temperature of a skin surface of the vehicle occupant, for example a temperature of the skin of the face. The hybrid thermal imaging camera can be set up to generate both an optical image by means of visible light and by means of electromagnetic radiation with a wavelength between 0.7 μm and 1000 μm. The night vision device can be set up to generate an optical image of the vehicle occupant without using an illumination in the visible range. In this way, the vehicle occupant can be observed without disturbing them with lighting.

For example, the camera system can have an image intensifier in the visible range. This means that there is no need for lighting in the visible area. For example, the camera system can be set up to convert infrared radiation and / or NIR radiation into visible light. The lighting device can be set up to illuminate an interior space and / or the vehicle occupant and / or part of the vehicle occupant, for example with visible light and / or NIR radiation, in particular in order to improve the generation of optical images. The lighting device can comprise an infrared headlight. The lighting device can be invisible to the vehicle occupants.

The optical filter can for example be a polarization filter and / or a color filter, in particular an edge filter. The motion detector can be set up to detect a movement of the vehicle occupant. The motion detector can be set up to receive microwave radiation and / or infrared radiation and / or ultrasound and, for example, also to transmit it.

The camera system can have two to six, in particular two to four, in particular three cameras. The camera system can have four cameras, for example. The camera system can for example have at least two cameras, for example two cameras in the visible area, for example to get spatial information.

The camera system can have a first NIR camera, for example. In addition, the camera system can have a second NIR camera. In addition, the camera system can have a camera in the visible range and / or a hybrid thermal imaging camera. The first NIR camera can, for example, be arranged next to the second NIR camera, in particular in order to image the vehicle occupant or part of the vehicle occupant from different viewing angles and / or with different foci.

The recorded vital value can be an optical skin property. The recorded vital sign can have a dynamic variation of the optical skin property. The optical skin property can be selected from the group comprising a degree of reflection of the skin, in particular a reflectance, a degree of absorption of the skin, a degree of transmission of the skin, a skin color, an emission spectrum of the skin.

The camera system can have at least one color filter, for example in order to detect the skin color, in particular a reddening of the skin of the vehicle occupant. The control device can be set up to infer a reddening of the skin and / or paleness of the vehicle occupant from the skin color, for example to detect an MSI and / or to monitor motion sickness.

The degree of reflection of the skin and / or the degree of absorption of the skin and / or the degree of transmission of the skin can relate to electromagnetic waves in a defined wavelength range, for example to visible light and / or infrared light and / or microwave radiation.

The control device can be set up, for example, to detect and / or monitor perspiration of the vehicle occupant from the degree of reflection of the skin. The control device can be set up to infer an MSI from the recorded perspiration. For example, the camera system can be set up to detect the degree of reflection of the skin of the vehicle occupant on the face, for example on the forehead.

The control device can be set up to infer the severity of a motion sickness of the vehicle occupant from the optical skin property. For example, the control device can be set up to infer the MSI from the skin property, for example from the degree of reflection.

The data, in particular the data generated by the camera system, can have, for example, the optical skin property and / or an optical image of the vehicle occupant. The control device can be set up to recognize a body part of the vehicle occupant in the optical image. The body part can be selected from a group of body parts comprising a face, an eye, a pupil, a hand, a neck, a cheek, a forehead and a chin. For example, the control device can be set up to infer an identity of the vehicle occupant from data from the camera system.

The evaluation device can be set up to carry out a self-learning algorithm. For example, the evaluation device can be set up to process data from the camera system by means of the self-learning algorithm.
The evaluation device can be set up to determine a duration of the training phase. For example, the evaluation device can be set up to classify the learning process of the self-learning algorithm. For example, the evaluation device can be set up to analyze the occurrence of different driving dynamics. The duration of the training phase can take place, for example, on the basis of a comparison between target specifications and actual specifications with different driving dynamics and / or recorded vital values. The evaluation device can, for example, be set up to end the training phase as soon as the self-learning algorithm has learned a sufficient number or all of the relevant driving maneuvers based on a sufficiently high number of data, in particular in such a way that motion sickness is prevented and / or a vital sign is predicted with sufficient accuracy can. The evaluation device can be set up to end the training phase as soon as necessary GSR information of the vehicle occupant can be assessed individually. This can ensure that the training phase only lasts as long as is necessary. This can increase comfort for the vehicle occupant.

The control device can be set up to determine a calculated vital value of the vehicle occupant from data collected during the training phase during a second time interval. The second time interval can, for example, directly follow the training phase. Alternatively or additionally, for example, after a training phase for a first vehicle occupant, a training phase for a second vehicle occupant can follow. The calculated vital sign can be a predicted vital sign. The control device can be set up to estimate necessary GSR information of the vehicle occupant during the second time interval, in particular from during the During the training phase, learned relationships between driving dynamics and a body reaction. For example, the training phase and the second time interval can at least partially overlap. For example, the control device can be set up to warn of motion sickness or to suppress motion sickness during the second time interval.

The control device can be set up to record driving dynamics and a body reaction occurring to the respective driving dynamics during the training phase. As soon as the system has been learned in, the first sensor is preferably no longer required, since a body reaction can be estimated for a vehicle occupant on the basis of the previously learned relationships between driving dynamics and vital signs.

The evaluation device can be set up to assign the recorded vital value of the vehicle occupant to a driving dynamics value. The evaluation device can be set up to form a value pair from the recorded vital value and the driving dynamics value. The evaluation device can be set up to form a value pair for different times. Using the self-learning algorithm, a prediction for a vital value from a driving dynamics value can be achieved from the pair of values. For example, the evaluation device can be set up to assign a driving dynamics value to a calculated vital value. The driving dynamics value can result, for example, from randomly occurring driving dynamics. As an alternative to this, the driving dynamics value can result from predetermined driving dynamics, for example by means of a predetermined course and / or predetermined driving dynamics values. This enables a prediction of the course of motion sickness to be achieved.

The control device can have a third interface. The third interface can, for example, be identical to the first interface and / or to the second interface. Alternatively, the third interface can be different from the first interface and / or the second interface. The third interface can be set up to receive the driving dynamics value from a second sensor. The second sensor can for example be a speed sensor, for example a speed sensor on a wheel of the motor vehicle. The second sensor can be selected, for example, from a group of sensors comprising a speed sensor, a temperature sensor, an acceleration sensor, a charging sensor, a speedometer, a GPS sensor and a microphone. This allows driving dynamics values to be made available to the control unit.

The second interface can be set up to send the signals to a vehicle system. For example, the second interface can be set up to send signals to a vehicle steering system and / or an engine and / or a transmission and / or a clutch device. The second interface can be set up to initiate an autonomous driving maneuver. The vehicle system can be, for example, a device that is set up to suppress or prevent motion sickness. For example, the vehicle system can be a device for notifying a vehicle occupant of a driving maneuver.

For example, the control device can be set up to carry out a countermeasure for the occurrence of motion sickness or for avoiding motion sickness of a vehicle occupant. For example, the control device can be set up to carry out an adaptation of a driving strategy of an automated vehicle, in particular an adaptation to an individual vehicle occupant. For example, the control device can be set up, based on a discrete maneuver planning, including, for example, staying in lane, changing lanes on the left, changing lanes on the right, double changing lanes on the left, double changing lanes on the right, normal deceleration, gentle deceleration, normal acceleration, a soft acceleration, a driving strategy of an automated vehicle, adapting a driving maneuver to an individual vehicle occupant. The evaluation device can be set up to give a recommendation for avoiding certain driving maneuvers in certain driving dynamics ranges. This can increase comfort. This can prevent or alleviate motion sickness. A mood of a vehicle occupant can be increased.

The control device can be set up to detect motion sickness. Furthermore, the control device can be set up to evaluate motion sickness. Furthermore, the control device can be set up to prevent motion sickness.

The control device can be set up to carry out a measure to prevent motion sickness. In particular, the control device can be set up to adapt a driving strategy, in particular to an individual vehicle occupant. Adaptation of the driving strategy can include, for example, route planning and / or maneuver selection and / or planning of a local trajectory. A measure to prevent motion sickness can include, for example, adapting a chassis. An adaptation of a chassis can for example an adaptation of an air conditioning system and / or a seat position and / or a background noise and / or lighting and / or ventilation and / or heating. A measure to prevent or reduce motion sickness can include, for example, a subliminal change in the interior space, for example acoustic feedback and / or haptic feedback.

For example, the control device, in particular the second interface, can be set up to inform a vehicle occupant about future driving dynamics and / or current driving dynamics. For example, the control device can be set up to control multimodal sensory stimulation of a vehicle occupant. A multimodal sensory stimulation can stimulate one or more senses of the vehicle occupant, for example a sense of touch, both on a body surface or inside the body, and / or a sense of hold and / or a sense of sight. A multimodal sensory stimulation can prevent or suppress a discrepancy between a sensory impression of a vehicle occupant and driving dynamics, in particular in order to prevent or alleviate motion sickness.

For example, the second interface can be set up to send signals to the vehicle occupant in order to provide information about current or future driving dynamics, for example by means of displays on suitable output devices, for example on the screen. The output device can, for example, be set up to inform a vehicle occupant subliminally, in particular with regard to driving dynamics. The output device can for example be set up to acoustically stimulate a vehicle occupant.

For example, future driving dynamics can be displayed by means of an acoustic system, for example by using headphones and / or structure-borne noise and / or by acoustic free-field stimulation. For example, the second interface can be set up to transfer driving dynamics to an integrated system which is set up to acoustically map an intensity of driving dynamics, for example by using time-frequency modulation. For example, the control device can be designed to use a binaural stimulation technique, that is to say that affects both ears, for example to produce binaural beats, for example to trigger a lateral noise illusion in the head of a vehicle occupant. In this way, a background noise can be generated in order to suppress motion sickness.

As an alternative or in addition to noise stimulation, the control unit can be set up to convey information to the vehicle occupant by means of haptic stimulation. These stimulations can be used to map current or future driving dynamics on the body of a vehicle occupant, for example at suitable points on the skin, preferably independently of clothing, for example by using ultrasound, for example by means of a haptic projection by ultrasonic waves in the air. An intensity of current or occurring driving dynamics can, for example, be represented by different patterns and / or a rate of a change in a pattern and / or different intensities of a stimulation, in particular a haptic stimulation. For example, in a left turn, an ultrasound pattern can be imaged on a circular area of the skin of a vehicle occupant, for example as a circle with a radius that increases with the intensity of the driving dynamics, for example increasing with increasing centrifugal force.

Alternatively or in addition, the control device can be set up to control visual stimulation of a vehicle occupant. For example, the control device can be set up to control a visual system. By means of the visual system, occurring driving dynamics can be visually represented, for example by using ambient light and / or light sources and / or screen light. For example, the control device can be set up to display driving dynamics by means of the vehicle system, for example including an optical system, in color and / or as a spatial appearance.

The control device can be set up to control subliminal stimulation of a vehicle occupant. In the case of subliminal stimulation, subliminal variants of the techniques discussed above, for example acoustic stimulation and / or optical stimulation and / or haptic stimulation, can be used. Here, the vehicle occupant should ideally not be aware of the stimulation. The subliminal stimulation should be carried out by means of the control device in such a way that motion sickness is prevented or suppressed, for example by using binaural beats.

The second interface can be set up, for example, to send signals from the evaluation device to a haptic board. The haptic board can be a device that can trigger haptic sensations in a vehicle occupant, for example by transmitting sound via the air, in particular by using focused ultrasonic waves to enable contactless feedback to a vehicle occupant.

The inventors have recognized that an electroencephalogram can be influenced by ultrasound. The ultrasonic waves can, for example, trigger vibrations of objects in the motor vehicle. Somatosensory evoked potentials (SEPs) can be triggered by stimulation using ultrasonic waves in the air. For example, a palm of a vehicle occupant can be stimulated by means of the ultrasonic waves, in particular by means of focal points in the air. As an alternative or in addition to the use of a haptic board, stimulations generated by body contact can also be controllable by means of the control device. A device for haptic stimulation can use focused ultrasound waves, for example to generate patterns through constructive interference. A pattern generated in this way can, for example, be a focal point in the air. For example, the ultrasonic waves can be modulated with a frequency that can be detected, for example, by receptors in the skin of a vehicle occupant.

The control device can be set up to control countermeasures against motion sickness. The countermeasures can include, for example, compensation for missing visual information by means of sensory stimulation, for example as a function of the severity of the motion sickness. In order to use haptic stimulation as a countermeasure, a driving dynamics value can be converted into signals, for example control signals, by means of the evaluation device, for example to control two haptic devices.

The haptic devices can be arranged or can be arranged on the neck of a vehicle occupant, for example. The haptic devices can be set up to apply haptic signals to small circles on the neck of a vehicle occupant. A direction of a curve can, for example, specify whether a right haptic device or a left haptic device stimulates the vehicle occupant. An intensity of a stimulation can be influenced, for example, by a shape of a curve. For example, an intensity of the stimulation can be higher for a sharp curve than for a slight curve.

The control device can be set up to send signals to the haptic board by means of the second interface. The haptic board can be set up to generate haptic sensations in the air, which can serve as a countermeasure against motion sickness. Using the haptic board, missing visual information can be proactively compensated for by sensory information. For example, the driving dynamics of a curve can be transferred into a position and / or an intensity of a stimulation on the neck of a vehicle occupant, in particular generated by ultrasonic waves in the air.

For example, the control device can be set up to trigger binaural beats, in particular by means of the second interface. With the binaural beats, motion sickness can be alleviated or prevented. As an alternative to binaural beats, other acoustic stimulations can be triggered by means of the control device. An acoustic illusion can be created using binaural beats and / or conventional acoustic stimulations. Binaural beats do not physically exist outside of a person's head, for example a vehicle occupant. Binaural beats result from the impact on two ears of a vehicle occupant by means of an auditory system in the vehicle occupant's brain. Binaural beats can, for example, be generated by two slightly different sine tones, for example with frequencies of 400 Hz and 410 Hz, separated in one ear. As a result, the person, for example the vehicle occupant, hears a pulsating tone, the frequency corresponding to a mean value of the two frequencies. Depending on the frequency of the binaural beat and sometimes also different from person to person, binaural beats can be perceived by the vehicle occupant as an illusion of movement.

Binaural beats can have positive effects on cognitive functions or emotions, for example increased attention, improved memory, increased creativity or improved sleep quality. For example, a continuous noise or a speech signal can be manipulated by means of the evaluation device in such a way that they have short, volatile binaural beats. In the case of a modified speech signal, a person who only hears in one ear can only receive a normal sentence. Only when both ears are played simultaneously can the person, for example the vehicle occupant, perceive a binaural beat.

Countermeasures to prevent motion sickness may include implementing binaural beats. Two tones with slightly different frequencies can be generated continuously by two different loudspeakers on the head of a vehicle occupant. For example, if no curve can be detected, a volume and / or a frequency of the two loudspeakers can be very low. For example, the vehicle occupant can continuously perceive binaural beats. In the case of a curve, the control unit can change the vehicle occupant's perception by applying binaural beats. Depending on a direction of the curve, one of the loudspeakers can, for example, increase its volume, which can result in a strong spatial expression. For example, a frequency of the two loudspeakers can be increased depending on the curvature of a route. In this way, an intuitive impression of the curvature of the route can be generated. This can prevent or suppress motion sickness. The countermeasure can be selected from a group of measures comprising a subliminal internal action, for example an acoustic feedback and / or a haptic feedback, a chassis conditioning, and an adaptation of a driving strategy, for example an adaptation of a route planning and / or a clever selection of maneuvers and / or coordinated local trajectory planning.

The control device can have a memory, for example. The memory can be set up to store a data record. The data record can, for example, each have an individual data record for each vehicle occupant. The data record can, for example, have the optical skin property or a data pair comprising the optical skin property and an associated driving dynamics value. The data record can, for example, have the data passed from the first sensor via the first interface to the evaluation device during the training phase. The data record can for example have a mathematical function that was generated during the learning phase by means of the self-learning algorithm. The data record can for example comprise the data pairs. The memory can be set up to store scenario functions, in particular scenario vehicle occupants, each in an individual data record. The memory can for example be arranged in the motor vehicle. Alternatively, the memory can be arranged outside of the vehicle. The memory can be implemented, for example, by a cloud. The memory can be set up to store data sets selected from a group of information comprising arithmetic operations, an autonomous algorithm, models, driving dynamics, driving dynamics values, calculated MSI and MSI.

The control device can be set up to call up the individual data record of a vehicle occupant, for example when starting a journey. For example, the control device can be set up to send an image of a vehicle occupant to the evaluation unit, for example by means of the first sensor. The evaluation unit can be set up to carry out face recognition. As a result, the control device can be set up to recognize a vehicle occupant when entering a motor vehicle and to access the individual data records of the vehicle occupants accordingly. The individual data record can contain, for example, the necessary data to carry out individually suitable countermeasures against travel sickness for each vehicle occupant or to prevent or suppress travel sickness by selecting the appropriate driving dynamics. The individual data record can for example be a trained data record. For example, the control device can be set up to store an individual data record for a specific vehicle type for several vehicle occupants. This can be advantageous, for example, in car sharing fleets, for example if an individual database is available for each customer, so that an individual data record can be called up from a vehicle when a user gets in.

The second sensor can be arranged on a body part of the vehicle occupant, for example, during the training phase. The second sensor can be a wearable, for example. The second sensor can for example be arranged on a wrist or on a finger or on the head or on the upper arm of the vehicle occupant during the training phase.

The second sensor can be selected from a group of sensors comprising: a camera, a measuring device for detecting skin resistance, a blood / oxygen measuring device, a device for detecting an electroencephalogram, a handheld measuring device, a temporarily usable measuring device and a wearable. A wearable is a measuring device that can be worn on the body. As an alternative to this, the first sensor can, for example, be a camera or have a camera.

For example, the control device can be used to carry out an optical method for determining a dynamic variation of a skin surface of a vehicle occupant as a marker for the GSR and / or for a body temperature. The optical method can be non-contact and non-intrusive. The control device can be set up to carry out an optical method, wherein an electrodermal activity (EDA) can be approximately determined in the optical method. The control device can be set up to determine a skin temperature, for example by an optical method, for example from a reflectivity of the skin, in particular on the face of a vehicle occupant, for example by evaluating camera images from an “interior operation system” in the motor vehicle.

The control device can be set up to monitor a vehicle occupant, preferably during a training phase. The control device can be set up to control a detection of an electrodermal activity (GSR) and / or a temperature and / or an electrocardiogram (EKG). The GSR can be divided into two components. A first component is a background activity, which reacts slowly and is also called a tonic GSR. The second component is a rapidly changing component, which is also called a phase GSR. The tonic GSR can respond directly to motion sickness. The phasic GSR can be a physiological response to movement. The inventors have recognized that when motion sickness occurs, the tonic GSR increases just like the activity of the phasic GSR. Furthermore, the inventors have recognized that the phased GSR reacts directly to a specific driving situation. Temperature and electrodermal activity are important parameters for measuring motion sickness, particularly with regard to toxic hyperthesis.

The inventors have found that a temperature increases with deterioration of a subjective feeling with motion sickness. The temperature can for example be detected by means of a sensor, for example the first sensor, on a little finger of the hand, for example the left hand. Alternatively or additionally, the temperature can be recorded optically with a camera system. For example, the first sensor can be a camera system. In order to detect a heart rate and / or the GSR and / or a temperature and / or an acceleration, for example a single sensor can be used as the first sensor. The individual sensor can for example be a bracelet, for example a bracelet, which can monitor the heart rate and / or the GSR and / or the temperature and / or an acceleration. Using a camera system as the first sensor, a contactless detection and / or monitoring of motion sickness can be made possible. For example, the GSR can be estimated by changing an appearance of the skin through images. The inventors have recognized that there is a correlation between a subjective degree of severity of a motion sickness and a temperature at points on the face of a vehicle occupant.

The first sensor can be a camera system, for example. The camera system can for example have one to ten, in particular three to five, for example three or four cameras. For example, the camera system can have two near infrared (NIR) cameras. Near-infrared cameras can be advantageous over RGB image cameras, for example with regard to varying lighting while driving in the motor vehicle. Alternatively or additionally, the camera system can have one or two RGB cameras, for example. By using two NIR cameras and / or two RGB cameras, for example, a depth can also be estimated. The camera system can alternatively or additionally have a thermal imaging camera. The thermal imaging camera can be set up to detect a temperature, for example a temperature of a skin surface of a vehicle occupant.

For example, the control device can be set up to carry out face tracking or face recognition of a vehicle occupant. For example, face tracking can be done with one or more cameras. For example, the thermal imaging camera can be guided by means of the control device in such a way that the temperature of the vehicle occupant, in particular at elevated points on the skin, for example on the face, can take place. The information on the depth, which can be achieved, for example, by two NIR cameras, can also be helpful here. The control device can thus be set up to carry out an intrinsic image decomposition. This makes it possible to generate an image without lighting. In this way, for example, a GSR can be detected without contact. This allows motion sickness to be predicted, in particular contactlessly.

The recorded vital sign can have one or more measured variables selected from the group comprising a pulse, for example a heart rate, an electroencephalogram (EEG), a skin resistance, for example the phasic skin resistance and / or the tonic skin resistance, an electrocardiogram (EKG), a body temperature, a pupil size, an eyelid closure, a muscle tone, a breathing rate, a breathing volume, an oxygen saturation, a blood pressure, a face color, a body reaction and a facial expression.

The control device can be set up to determine an index for the severity of a motion sickness by means of the data from the first sensor, in particular by means of the recorded vital sign. The first sensor can be arranged on a finger, for example. For example, the first sensor can be a blood oxygen sensor on a finger. The first sensor can be, for example, an EEG sensor on a head of a vehicle occupant. The first sensor can for example be an optical sensor or have an optical sensor.

The control device can have a fourth interface, for example. The fourth The interface can be configured to send a request to remove the first sensor to the vehicle occupant. The request can be a message, for example. The request can be transmitted to a vehicle occupant acoustically and / or haptically and / or optically, for example. The request can be carried out, for example, by means of a device which can also be used for countermeasures to the motion sickness. The request to remove the first sensor can be sent, for example, at the end of the training phase. As a result, the vehicle occupant can remove the first sensor after the training phase, in particular in order to create privacy. As a result, the vehicle occupant only has to be monitored by the first sensor during the training phase. This can increase comfort.

Another aspect of the present invention relates to a use of the control device as described above in a motor vehicle.

• - Bereitstellen eines Steuergeräts zum Einsatz in einem Kraftfahrzeug, wobei das Steuergerät eine erste Schnittstelle, eine Auswertevorrichtung und eine zweite Schnittstelle aufweist,
• - Erhalten von Daten eines ersten Sensors über die erste Schnittstelle mittels der Auswertevorrichtung, wobei die Daten einen erfassten Vitalwert eines Fahrzeuginsassen umfassen, wobei der erfasste Vitalwert mittels des ersten Sensors ohne Körperkontakt zu dem Fahrzeuginsassen erfasst wird,
• - Auswertung von über die erste Schnittstelle erhaltenen Daten mittels der Auswertevorrichtung,
• - Versenden von Signalen von der Auswertevorrichtung mittels der zweiten Schnittstelle.

Another aspect of the present invention relates to a control method for use in a motor vehicle, with the steps:

• - Provision of a control device for use in a motor vehicle, the control device having a first interface, an evaluation device and a second interface,
• - Receiving data from a first sensor via the first interface by means of the evaluation device, the data including a recorded vital value of a vehicle occupant, the recorded vital value being recorded by means of the first sensor without physical contact with the vehicle occupant,
• - Evaluation of data received via the first interface by means of the evaluation device,
• - Sending signals from the evaluation device by means of the second interface.

The control method can be an optical method for determining a dynamic variation of the skin surface of the vehicle occupant, in particular as a marker for a GSR or a temperature. The control method can have one or more further steps, for example. The steps can for example be carried out in the order mentioned. One or more steps can overlap in time. For example, the control method can include initiating countermeasures to alleviate or prevent motion sickness and / or a step for identifying a vehicle occupant and / or for retrieving individual data from a database.

For example, the vehicle occupant can wear a wearable during the first 100 kilometers with which the phasic and / or tonic GSR can be measured. As soon as the self-learning algorithm has been learned on the basis of a sufficiently high number of data and also relevant driving maneuvers, the vehicle occupant can, for example, be requested to switch off and / or remove the first sensor. The new control unit can be able to assess the necessary GSR data of the vehicle occupant individually. A trained data record can be individual and in particular be stored for the respective vehicle type. This makes it possible to select individually coordinated driving maneuvers and to specifically carry out a vital value assessment during a journey and to react accordingly instantaneously to the body reactions of the vehicle occupant.

Another aspect of the present invention relates to a system for controlling a motor vehicle. The system comprises a control device as described above and a first sensor which is set up to detect a vital sign of a vehicle occupant without physical contact with the vehicle occupant. The first sensor can be designed as described above. The system can furthermore comprise the second sensor and / or a device for carrying out a countermeasure. The system can for example comprise the optical sensor, in particular the camera system.

• 1 eine schematische Darstellung eines ersten Ausführungsbeispiels des neuen Steuergeräts,
• 2 eine schematische Darstellung eines zweiten Ausführungsbeispiels des neuen Steuergeräts,
• 3 eine schematische Darstellung eines dritten Ausführungsbeispiels des neuen Steuergeräts,
• 4 eine schematische Darstellung eines vierten Ausführungsbeispiels des neuen Steuergeräts,
• 5 eine schematische Darstellung eines fünften Ausführungsbeispiels des neuen Steuergeräts,
• 6 eine schematische Darstellung eines sechsten Ausführungsbeispiels des neuen Steuergeräts,
• 7 eine schematische Darstellung eines ersten Ausführungsbeispiels des neuen Steuerverfahrens,
• 8 eine schematische Darstellung eines Kamerasystems für das neue System,
• 9 eine schematische Darstellung eines zweiten Ausführungsbeispiels des neuen Steuerverfahrens,
• 10 eine schematische Darstellung eines dritten Ausführungsbeispiels des neuen Steuerverfahrens,
• 11 eine Übersicht möglicher Gegenmaßnahmen für ein Ausführungsbeispiel des neuen Steuerverfahrens,
• 12 eine schematische Darstellung eines vierten Ausführungsbeispiels des neuen Steuerverfahrens,
• 13 eine schematische Darstellung eines fünften Ausführungsbeispiels des neuen Steuerverfahrens,
• 14 eine schematische Darstellung eines sechsten Ausführungsbeispiels des neuen Steuerverfahrens,
• 15 eine schematische Darstellung eines siebten Ausführungsbeispiels des neuen Steuerverfahrens,

Exemplary embodiments of the invention are shown in the drawing and are explained in more detail in the following description. Show it:

• 1 a schematic representation of a first embodiment of the new control unit,
• 2 a schematic representation of a second embodiment of the new control device,
• 3 a schematic representation of a third embodiment of the new control unit,
• 4th a schematic representation of a fourth embodiment of the new control unit,
• 5 a schematic representation of a fifth embodiment of the new control device,
• 6th a schematic representation of a sixth embodiment of the new control device,
• 7th a schematic representation of a first embodiment of the new control method,
• 8th a schematic representation of a camera system for the new system,
• 9 a schematic representation of a second embodiment of the new control method,
• 10 a schematic representation of a third embodiment of the new control method,
• 11 an overview of possible countermeasures for an embodiment of the new tax method,
• 12th a schematic representation of a fourth embodiment of the new control method,
• 13 a schematic representation of a fifth embodiment of the new control method,
• 14th a schematic representation of a sixth embodiment of the new control method,
• 15th a schematic representation of a seventh embodiment of the new control method,

A first embodiment of the new control unit 10 is in 1 shown. The new control unit 10 has a first interface 12th on to get data from a first sensor 14th to obtain the data with a recorded vital sign 76 of a vehicle occupant 15th include. The control unit 10 furthermore has an evaluation device 16 on to the evaluation 38 from over the first interface 12th received data. The new control unit 10 furthermore has a second interface 18th to receive signals from the evaluation device 16 to send. The evaluation device 16 is set up to use a self-learning algorithm 90 perform. The control unit 10 is set up to use the self-learning algorithm 90 at the end 17th a training phase 19th to end.

The evaluation device 16 can be set up to have a duration 21st the training phase 19th , as in 7th shown to determine. The control unit 10 can be set up to get off during the training phase 19th collected data during a second time interval 23 a calculated vital sign 92 of the vehicle occupant 15th to investigate. The evaluation device 16 can be set up to use the recorded vital signs 76 of the vehicle occupant 15th a driving dynamics value 78 assign. The second interface 18th can be set up to send signals to a vehicle control system 30th to send. The first sensor 14th can during the training phase 19th on a body part of the vehicle occupant 15th be arranged or be arranged. The controller may include a second sensor 22nd exhibit. The second sensor 22nd can be selected from a group of sensors comprising a camera system 29 , a measuring device 32 for detecting a skin resistance, a blood-oxygen measuring device, a device for detecting an electroencephalogram, a hand-held measuring device, a measuring device to be used temporarily and a wearable. The second interface 18th can be set up to send the signals to a vehicle system 30th to send, for example to a transmission and / or to a steering system.

The camera system 29 can for example be a camera system 29 as in 8th or 4th be shown. The camera system 29 can be a first NIR camera 64 and a second NIR camera 66 exhibit. In addition, the camera system 29 a thermal imaging camera 68 exhibit. The components mentioned can communicate with one another, for example, in particular by means of the new control unit 10 . The camera system 29 can be a hybrid thermal imaging camera 70 exhibit or form.

The recorded vital sign 76 can have one or more measured variables selected from the group comprising a pulse, an electroencephalogram, a skin resistance, an electrocardiogram, a temperature 108 , in particular a body temperature, a pupil size, an eyelid closure, a muscle tone, a breathing rate, a breathing volume, an oxygen saturation, a blood pressure, a face color, a body reaction and a facial expression.

2 shows a second embodiment of the new control device 10 . The second embodiment can look like the first embodiment 1 be designed. In addition to the first embodiment, the embodiment shows 2 a third interface 20th on. The third interface 20th is set up to the driving dynamics value 78 from a second sensor 22nd to obtain.

3 shows a third embodiment of the new control unit 10 . The third embodiment according to 3 can in principle like the first embodiment 1 be designed. In addition to the first embodiment, the third embodiment has a memory 24 on. The memory 24 is set up to a record 26th to save.

The memory 24 can be set up to have an individual data record for several people 27 to save. The multiple people can be, for example, multiple vehicle occupants, for example multiple people who are in the motor vehicle at the same time 11 are or are alternately in the motor vehicle 11 are located. The control unit 10 can be set up to at the individual data record when starting a journey 27 of a vehicle occupant 15th retrieve.

4th shows a fourth embodiment of the new control unit 10 . The fourth embodiment of the new control unit 10 can for example look like the third embodiment 3 be designed. In addition to the embodiment according to 3 the fourth embodiment shows a camera system 29 . The camera system 29 can be configured to accommodate the vehicle occupant 15th map. The control unit 10 can be set up to use the data from the camera system 29 the vehicle occupants 15th to recognize afterwards the individual data record 27 of the vehicle occupant 15th to retrieve and / or to record the data, for example a temperature 108 , especially a skin temperature.

5 shows a fifth embodiment of the new control device 10 . The fifth embodiment according to 5 can in principle be designed like the third embodiment. The embodiment according to 5 additionally has a fourth interface 28 on. The fourth interface 28 is set up to prompt for dismantling 31 of the first sensor 14th to the vehicle occupants 15th to send.

6th shows a sixth embodiment of the new control device 10 . The control unit 10 after 6th can in principle like one of the previously described exemplary embodiments of the new control units 10 be trained. In addition, the control unit is 10 after 6th set up to run a countermeasure 46 to control. For example, the control unit 10 be designed to provide acoustic feedback 72 to the vehicle occupants 15th to give, especially to a motion sickness 95 to weaken or suppress. The acoustic feedback 52 can for example by means of headphones 53 be executable, for example by means of binaural beats.

1 to 6th each also show exemplary embodiments of the new system 72 . The new system 72 has a control unit 10 as described above and the first sensor 14th on, the first sensor 14th is set up to include a recorded vital sign 76 of a vehicle occupant 15th capture. For example, the new systems 72 also comprise one or more further elements, in particular all of the elements shown in the respective figure.

7th shows a first embodiment of the new control method 74 . 7th shows in particular a timeline or a distance covered.

The timeline has two training phases 19th on. The left training phase 19th can for example be a training phase 19th for a first vehicle occupant 15th be. The right training phase 19th can for example be a training phase 19th be for a second vehicle occupant. For example, the first vehicle occupant 15th and the second vehicle occupant the motor vehicle 11 use alternately. The timeline also shows two second time intervals 23 . The new control method can, for example, be designed in such a way that the second time interval 23 completely with the training phase 19th overlaps. 7th shows the durations 21st of the two training phases 19th as well as the ends 17th the training phases 19th . Furthermore shows 7th in each case the point in time for the request to remove 31 of the first sensor 14th . This increases the comfort for the occupant. For example, during the training phases 19th and / or during the second time interval 23 by means of the first sensor 14th the recorded vital sign 76 without body contact of the first sensor 14th to the vehicle occupant 15th to be determined.

• - Bereitstellen 34 eines Steuergeräts 10 zum Einsatz in einem Kraftfahrzeug 11, wobei das Steuergerät 10 eine erste Schnittstelle 12, eine Auswertevorrichtung 16 und eine zweite Schnittstelle 18 aufweist,
• - Erhalten 36 von Daten eines ersten Sensors 14 über die erste Schnittstelle 12 mittels der Auswertevorrichtung 16, wobei die Daten einen erfassten Vitalwert 76 eines Fahrzeuginsassen 15 umfassen, wobei der erfasste Vitalwert 76 mittels des ersten Sensors 14 ohne Körperkontakt zu dem Fahrzeuginsassen 15 erfasst wird,
• - Auswertung 38 von über die erste Schnittstelle 12 erhaltenen Daten mittels der Auswertevorrichtung 16, und
• - Versenden 44 von Signalen von der Auswertevorrichtung 16 mittels der zweiten Schnittstelle 18.

The new tax method 74 corresponding 7th , 9 , 10 and 12th have the following steps:

• - Provide 34 of a control unit 10 for use in a motor vehicle 11 , the control unit 10 a first interface 12th , an evaluation device 16 and a second interface 18th having,
• - Receive 36 of data from a first sensor 14th over the first interface 12th by means of the evaluation device 16 , the data being a recorded vital sign 76 of a vehicle occupant 15th include, the recorded vital sign 76 by means of the first sensor 14th without physical contact with the vehicle occupant 15th is captured,
• - evaluation 38 from over the first interface 12th obtained data by means of the evaluation device 16 , and
• - To ship 44 of signals from the evaluation device 16 by means of the second interface 18th .

• - Durchführen 40 eines selbstlernenden Algorithmus 90 mittels der Auswertevorrichtung 16, und/oder
• - Beenden 42 des selbstlernenden Algorithmus 90 am Ende 17 einer Trainingsphase 19.

For example, the new method can also include one or more further steps, for example:

• - Carry out 40 a self-learning algorithm 90 by means of the evaluation device 16 , and or
• - Break up 42 the self-learning algorithm 90 at the end 17th a training phase 19th .

The evaluation step 38 from over the first interface 12th data obtained by means of the Evaluation device 16 can for example be identical to the implementation 40 a self-learning algorithm 90 by means of the evaluation device 16 , such as in 9 shown.

10 shows an embodiment of the new control method 74 with the additional step of taking a countermeasure 46 . Possible countermeasures 46 are for example in 11 shown. In the countermeasures 46 it is especially countermeasures 46 to improve or prevent motion sickness 95 . The countermeasure 46 can be selected from a group of measures, such as in 11 shown, such as a subliminal internal action 48 , for example an acoustic feedback 52 and / or haptic feedback 50 , a chassis conditioning 62 , and an adaptation of a driving strategy 54 , for example an adaptation of a route planning 56 and / or a clever maneuver selection 58 and / or coordinated local trajectory planning 60 .

12th shows another embodiment of the new control method 74 . The large boxes show the preservation from top to bottom 36 of data from the first sensor 14th and / or the second sensor 22nd , i.e. the recorded vital signs 76 , for example raw signals of a GSR value and driving dynamics values 78 . Both values can be referred to as input, for example. When evaluating 38 the data can, for example, perform various arithmetic operations 86 be performed. For example, a tonic GSR can be obtained from the raw data of the GSR values, for example by unfolding 80 and a phased GSR 82 be determined. From the values for driving dynamics 78 can for example be a trigger 84 , for example a time of acquisition, can be extracted. For example, in the evaluation step 38 check whether the trigger 84 it makes sense, for example, whether he is referring to the raw data of the respective recorded vital signs 76 , especially GSR values, fits. During the evaluation step 38 can do arithmetic operations in particular 86 be performed. In the arithmetic operation 86 For example, it can be determined whether the trigger 84 useful, for example useful.

From the values for driving dynamics 78 can still be a vector 88 be taken, for example an acceleration in all three spatial directions and / or a rotation by angles of the three spatial directions. The vector 88 the driving dynamics values 78 and / or the trigger 84 , for example a time, and the phase GSR 82 and / or the tonic GSR 80 can then, for example, when performing 40 the self-learning algorithm 90 be used. This step can, for example, be referred to as generating an output. The self-learning algorithm 90 can for example be a linear Gaussian regression. When using the algorithm 90 can for example be a calculated vital sign 92 be generated. From the calculated vital sign 92 can be a calculated MSI 94 be calculated.

13 and 14th show two further embodiments of the new control method 74 . The embodiment according to 13 particularly shows a control method 74 with haptic feedback 50 as a countermeasure 46 , for example by means of a haptic board 51 . The embodiment according to 14th particularly shows a control method 74 with an acoustic countermeasure 52 as a countermeasure 46 , for example using binaural beats. Otherwise, the exemplary embodiments can follow 13 and 14th be configured the same, for example as one of the control methods described above 74 . For example, in the tax procedure 74 after the 13 and 14th a self-learning algorithm 90 using a white box 102 be performed. Using the white box 102 can an MSI 94 be calculated, in particular using data from physiological measurements.

The data can be visual data, for example 104 and / or a heart rate 106 and / or a temperature 108 and / or an EDA 110 exhibit. From a calculated driving dynamics value 96 can be a curve intensity 100 and / or a direction of a curve 98 extracted and / or determined. The curve intensity 100 can choose an intensity 114 the countermeasure 46 , for example a volume and / or a vibration strength can be used. The direction of the curve 98 can for activation 112 a haptic board 51 or a specific loudspeaker, for example one of two loudspeakers. This provides an input 116 for the haptic board and / or for a loudspeaker. This can be a countermeasure 46 can be achieved, in particular a compensation for missing visual information to a motion sickness 95 to suppress.

15th shows another embodiment of the new control method 74 . This in 15th illustrated embodiment of the new control method 74 can at least partially like one of the new control methods described above 74 be designed. 15th shows on a left how a vehicle occupant 15th through autonomous driving 117 , in particular by driving dynamics, describable by driving dynamics values 78 , can be affected, in particular resulting in motion sickness 95 . With the new tax procedure 74 can do one or more arithmetic operations 86 , in particular computational models, selected from a group of models comprising an HFR model 118 , a 6DOF SVC model 120 , a multiple linear regression 122 , a self-learning algorithm 90 and a neural network 124 . The models mentioned take place in the order in which they are mentioned 15th from left to right, in terms of complexity 126 to and with the amount of the deviations 128 from, so be more precise.

The MSI 94 calculated and / or validated. As input for the arithmetic operations 86 can drive dynamics values 78 serve that result from autonomous driving 117 surrender. The new tax procedure 74 can store data in the memory 24 , for example a cloud 25th , include. In the memory 24 data sets can be selected from a group of information comprising arithmetic operations 86 , a self-learning algorithm 90 , Models, driving dynamics, driving dynamics values 78 , a calculated MSI 94 and an MSI. From the calculated MSI 94 can for example a driving strategy 54 and / or route planning 56 and / or a calculated driving dynamics value 96 to be determined.

List of reference symbols

10

Control unit

11

Motor vehicle

12

first interface

14th

first sensor

15th

Vehicle occupant

16

Evaluation device

17th

End of the training phase

18th

second interface

19th

Training phase

20th

third interface

21st

Duration of the training phase

22nd

second sensor

23

second time interval

24

Storage

25th

Cloud

26th

record

27

individual data set

28

fourth interface

29

Camera system

30th

Vehicle system

31

Request to dismantle

32

Measuring device for measuring skin resistance

34

Provision of a control unit

36

Obtaining data from a first sensor

38

Evaluation of data

40

Implementation of a self-learning algorithm

42

End the self-learning algorithm

44

Sending signals

46

Countermeasure

48

subliminal internal actions

50

haptic feedback

51

haptic board

52

acoustic feedback

53

headphone

54

Driving strategy

56

Route planning

58

Maneuver selection

60

local trajectory planning

62

Chassis conditioning

64

first NIR camera

66

second NIR camera

68

Thermal camera

70

Hybrid thermal imaging camera

72

system

74

Tax procedure

76

recorded vital sign

78

Driving dynamics values

80

tonic GSR

82

phasic GSR

84

Trigger

86

Arithmetic operations

88

vector

90

self-learning algorithm

92

calculated vital sign

94

calculated MSI

95

travel sickness

96

calculated driving dynamics value

98

Direction of a curve

100

Curve intensity

102

White box

104

Visual data

106

Heart rate

108

temperature

110

EDA

112

activation

114

Choosing an intensity

116

Input

117

Autonomous driving

118

HFR model

120

6DOF SVC model

122

Multiple linear regression

124

Neural network

126

complexity

128

Deviations

QUOTES INCLUDED IN THE DESCRIPTION

This list of the documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent literature cited

• US 2017/0313326 A1 [0003]

Non-patent literature cited

• ISO2631-1 [0050]

Claims (15)

Control device (10) for use in a motor vehicle (11), comprising: - A first interface (12) to receive data from a first sensor (14), the data including a recorded vital sign (76) of a vehicle occupant (15), - an evaluation device (16) for evaluating (38) data received via the first interface (12), - A second interface (18) to send signals from the evaluation device (16), the recorded vital sign (76) being recorded by means of the first sensor (14) without physical contact with the vehicle occupant (15). Control unit after Claim 1 wherein the first sensor (14) comprises an optical sensor (29, 64, 66, 68, 70). Control device according to one of the preceding claims, wherein the first sensor (14) has a camera system (29). Control device according to the preceding claim, wherein the camera system (29) has one or more elements selected from a group comprising: a receiver for electromagnetic waves (29, 64, 66, 68, 70), a transmitter for electromagnetic waves, a camera, a NIR camera (64, 66), a thermal imaging camera (68), a hybrid thermal imaging camera (70), a night vision device, a lighting device, a light amplifier, an optical filter and a motion detector. Control device according to one of the two preceding claims, wherein the camera system (29) has two to six cameras. Control device according to one of the three preceding claims, wherein the camera system (29) has a first NIR camera (64) and a second NIR camera (66). Control device according to one of the preceding claims, wherein the detected vital sign (76) is an optical skin property, the optical skin property being selected from a group comprising: a degree of reflection of the skin, a degree of absorption of the skin, a degree of transmission of the skin, a skin color, an emission spectrum of the skin. Control device according to the preceding claim, wherein the control device (10) is set up to infer the severity of a motion sickness of the vehicle occupant from the visual skin property. Control device according to one of the preceding claims, wherein the data have an optical image of the vehicle occupant, the control device (10) being set up to recognize a body part of the vehicle occupant in the optical image, the body part being selected from a group of body parts comprising: a face, an eye, a pupil, a hand, a neck, a cheek, a forehead, and a chin. Control device according to one of the preceding claims, wherein the evaluation device (16) is set up to carry out a self-learning algorithm (90). Control device according to one of the preceding claims, wherein the control device (10) has a memory (24), the memory (24) being set up to store a data set (26). Control device according to the preceding claim, wherein the memory (24) is set up to store an individual data set (27) for several people, the control device (10) being set up to store the individual data set (27) of a vehicle occupant when starting a journey. 15). Use of the control device (10) according to one of the preceding claims in a motor vehicle (11). Control method for use in a motor vehicle (11), with the steps: - Providing (34) a control device (10) for use in a motor vehicle (11), the control device (10) having a first interface (12), an evaluation device (16) and a second interface (18); - Receiving (36) of data from a first sensor (14) via the first interface (12) by means of the evaluation device (16), the data including a recorded vital value (76) of a vehicle occupant (15), the recorded vital value (76) is detected by means of the first sensor (14) without physical contact with the vehicle occupant (15); - Evaluation (38) of data received via the first interface (12) by means of the evaluation device (16); and - Sending (44) of signals from the evaluation device (16) by means of the second interface (18). System (72) for controlling a motor vehicle (11) comprising: - a control device (10) according to one of the Claims 1 to 12th ; and - a first sensor (14) which is set up to measure a recorded vital sign (76) of a vehicle occupant (15) to be detected without physical contact with the vehicle occupant (15).

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