EP4058319A1

EP4058319A1 - Method for displaying information on a human-machine interface of a motor vehicle, computer program product, human-machine interface, and motor vehicle

Info

Publication number: EP4058319A1
Application number: EP20800928.2A
Authority: EP
Inventors: Michael Wagner; Frank Jordan; Anna Paetzold; Frank Bonarens
Original assignee: PSA Automobiles SA
Current assignee: Stellantis Auto SAS
Priority date: 2019-11-11
Filing date: 2020-11-04
Publication date: 2022-09-21
Also published as: DE102019217346A1; WO2021094168A1; CN114728584A; DE102019217346B4

Abstract

The invention relates to a method for controlling a human-machine interface (4) of a motor vehicle (2), said human-machine interface (4) having at least one display device (8, 10, 12) for displaying information (34, 36, 38, 40, 42, 44). At least one drive situation parameter is ascertained which characterizes a drive situation of the motor vehicle (2), and a load parameter is determined on the basis of the ascertained drive situation parameter using an algorithm (20), said load parameter characterizing the cognitive load of a driver (6) of the motor vehicle (2) so that the display device (8, 10, 12) information density of information to be displayed by the display device (8, 10, 12) is adapted according to the load parameter, wherein the algorithm (20) comprises a data set with drive situation parameters, each drive situation parameter is assigned to a load parameter, and the load parameters are based on at least one measured variable which characterizes the behavior of the driver. The invention additionally relates to a computer program product, to human-machine interfaces, and to a motor vehicle.

Description

METHOD FOR PRESENTING INFORMATION ON A HUMAN MACHINE INTERFACE OF A MOTOR VEHICLE,

COMPUTER PROGRAM PRODUCT, HUMAN-MACHINE INTERFACE AND MOTOR VEHICLE

A method for displaying information on a human-machine interface of a motor vehicle, a computer program product, human-machine interfaces and a motor vehicle are described here.

Methods for displaying information on a human-machine interface of a motor vehicle, computer program products, human-machine interface and motor vehicles of the type mentioned at the beginning are known in the prior art.

The amount of information that a driver can grasp saliently and quickly depends on the cognitive load a driver is exposed to during a current driving situation. The cognitive load reflects how much the driver is mentally stressed in order to fully grasp a situation, in particular a driving situation, and to be able to react correctly. The more information a driver has to absorb, the higher his cognitive load. The more complex a driving situation, the more the driver has to concentrate. On a simple route with little traffic, less attention is required than on a busy, winding route that is driven quickly. It is also important to avoid cognitive underloading of the driver. On a straight stretch with little traffic at constant speed, there is a risk of the driver losing concentration. In the former case, it is easy for the driver to record and evaluate a large amount of information from a human-machine interface. In the second case, the driver could be overwhelmed by too much information and no longer pay sufficient attention to the traffic. In the third case of vigilance, it is more difficult for the driver to absorb changing and added information and to process it appropriately.

A driving situation describes the interaction between the driver, the vehicle and the environment. Adaptive displays are known per se. In its simplest form it is known by day and to use different display modes at night, for example a night mode on a display. It is also known to vary the color of the display. Furthermore, it is known to configure the display as a function of the driving mode, for example differently for sports cars for road use and for racing track operation. There are also variable displays in which information can be shown in different sizes. The corresponding adaptations of the displays are made either automatically, for example as a function of the ambient brightness, or manually, for example when selecting a display mode or a display color.

From DE 102007 058437 A1 a method for conveying information to the driver of a motor vehicle is known in which optical information displays are brought to the driver by means of a head-up display, the overall display of the head-up display being a group of neighboring ones Includes display fields in which information displays can be displayed, each element of a set of possible information displays for display being assigned to one of the display fields, with characteristic driving situation parameters of the current driving situation being determined, with a priority for each of the display fields depending on the characteristic driving situation parameters Information displays assigned to this display field are carried out and the content of each display field is determined as a function of the prioritization made for this display field.

The driving situation parameters known from the prior art can sometimes correlate coincidentally with the driver's cognitive load, but there are also situations in which a large amount of information would be displayed even though the driver's cognitive load is high. In these cases, the amount of information displayed could overwhelm the driver.

Thus, the task arises of developing methods for displaying information on a human-machine interface of a motor vehicle, computer program products, human-machine interface and motor vehicles of the type mentioned in such a way that the cognitive load of a driver is determined more directly and thus more precisely can be and the man-machine interface can be confi gured accordingly. Another task is to be able to take into account the individual cognitive performance of each specific driver. The object is achieved by a method for displaying information on a human-machine interface according to claim 1, a computer program product according to the independent claim 10, a human-machine interface according to the independent claim 11, a human-machine interface according to the neighboring claim 12 and a motor vehicle according to the independent claim 15. Further refinements and developments are the subject of the dependent claims.

A method for controlling a man-machine interface of a motor vehicle is described below, the man-machine interface having at least one display device for displaying information, at least one driving situation parameter being determined which characterizes a driving situation of the motor vehicle, and in Depending on the determined driving situation parameter, an algorithm is used to determine a load parameter that characterizes a cognitive load of a driver of the motor vehicle, so that an information density of the at least one display device is adapted to information to be displayed by means of the display device as a function of the load parameter, the algorithm including a data record Includes driving situation parameters, each driving situation parameter being assigned a load parameter, the load parameters being based on at least one measured variable that indicates a behavior d characterize it driver.

By determining the cognitive load of a driver and choosing the appropriate information density, it can be ensured that the driver is only shown as much information as he can process. An impairment of the information processing capacity of the driver by too much and changing information can be avoided and the driver has more mental resources free to devote himself to the traffic situation.

The most relevant information can be selected depending on the situation, for example navigation information when the driver is about to reach a junction, speed information when the driver is driving too fast or too slowly, or a warning when, for example, there is traffic requires increased attention. In a first further refinement, it can be provided that a behavior parameter is also determined which characterizes the current behavior of the driver, the information density of the information to be displayed on the display device being varied taking into account the behavior parameter determined.

The driver's behavior is a reliable measure of the driver's cognitive load. This allows better adaptation of the information density to the current needs of the driver.

In a further refinement, provision can be made for calculating the parameter to include a prediction of the driver's cognitive load at a given time, in a given time span and / or a given distance.

As a result, a prediction can be made about the cognitive load to be expected shortly and a suitable display configuration can be selected at an early stage as a function of the cognitive load to be expected. In this way, a selection of the displayed information on the most important information can be achieved even before a situation requiring attention occurs.

In a further refinement, it can be provided that, in normal ferry operation of the motor vehicle, measured variables pertaining to the driver are recorded and the driver is categorized into one of several user type classes, the information density being adapted as a function of the user type class of the driver.

It is known that different drivers have different cognitive capabilities depending on different aspects and are stressed to different degrees by different driving situations. A novice driver needs more attention and therefore more cognitive effort than an experienced driver. There are also certain differences, for example age-related, in the ability to absorb and process information between different drivers. It is also known that the duration of the journey has an influence on how attentive a driver is. Such measured variables can be recorded within the scope of this embodiment, whereby a display configuration tailored to the driver can be selected can.

This refinement can be used to provide different sets of information for different types of drivers. Some drivers can process a larger amount of information than others, so that it is possible to prevent those drivers who cannot process so much information from being overloaded by the human-machine interface.

In a further refinement, it can be provided that the driving situation-related input variables include measured variables relating to the driver, the motor vehicle and / or the environment.

Environment-related measured variables can include road layout, traffic, weather, time of day and the like, driver-related measured variables can include interactions with the human-machine interface, viewing direction, driving behavior and the like, motor-vehicle-related measured variables can include dynamics, use of assistance systems and the like.

It has been shown that these parameters are suitable for predicting a cognitive load for the driver. The faster the driver drives, the more attention he has to devote to the road. The more curvy a road, the more steering inputs are required, which cognitively demand the driver. The more road users there are on the road, the more attention is required because the driver has to observe the behavior of the other road users and, if necessary, react to it. For example, if a motor vehicle driving in front of the driver brakes, the driver must also brake. Monotonous driving situations such as stretches without bends with few road users can reduce the driver's attention and concentration. This can affect the driver's responsiveness.

In a further refinement, it can be provided that at least two display configurations are provided which have a different information density, the information density being adapted by selecting a display configuration.

More than two different display configurations can be provided can differ in more than one parameter dimension in some configurations. Different display configurations can display different amounts of information, display some information in different sizes and change the position of information, whereby it should be noted that a driver usually suspects information to be in a certain location, for example a speed display, a rotational speed, a range and the same. Thus, designs are conceivable in which information is picked out and displayed centrally in a corresponding size. The remaining information can be found in specific fields provided for the corresponding information.

In a further refinement, it can be provided that the man-machine interface has at least two display devices, on each of which a plurality of information relevant to a driver of the motor vehicle can be displayed, the algorithm being used to determine which information is displayed on which display device becomes.

Corresponding display devices can be, for example, a central information display, a cockpit display and / or a head-up display. In this way, very important information can be presented, for example, on a display device arranged close to the driver's field of vision, for example on a head-up display.

In a further refinement, provision can be made for the algorithm to be trained using a machine learning method in that test drives are carried out with a plurality of test drivers, with characteristic parameters for describing a driving situation being recorded and stored during the test drives, with the Test drivers receive input prompts to an input element of a man-machine interface during the test drives, with an input duration for performing the input, an input error rate and / or a number of operating steps being recorded and corrected with the characteristic parameters by the algorithm.

Machine learning processes make it possible to reliably assess very complex situations and, in particular, to make predictions about future cognitive loads in Dependency to make a prediction of the various driving situations. Such predictions are difficult to make with classically structured algorithms. In addition, algorithms of this type can be continuously improved during operation, so that the prediction of the cognitive load and, depending on the design, the categorization of the driver into different user type classes continue to improve.

The algorithm can be used in the form of artificial intelligence, which is a complex filter that can be further developed as discussed above.

In a further refinement, it can be provided that, during the test drives, further measured variables characteristic of the test driver's cognitive load are recorded and correlated with the characteristic parameters of the driving situation, the characteristic measured variables being a vehicle position in a lane, a number of times a lane has been crossed, a duration of the lane crossing, a deviation from a target speed, head movements, arm movements, steering wheel inputs, pedal inputs, and / or a direction in which the driver is looking.

The reaction time to complex tasks is a useful measure to determine the cognitive load of a driver in a given situation. The more attention the driver needs to drive in the current driving situation, the longer it takes to complete the tasks. Also the precision of the ride, i. H. How exactly the driver drives in his lane, how often he crosses the lane, how long he crosses the lane, etc. are an indication of how much the driver is currently cognitively stressed.

The monitoring of the driver himself, for example by means of an interior camera, can allow conclusions to be drawn about the current state of the driver. Quick changes of gaze and stabilization inputs via the pedals and the steering wheel can indicate a high visual-manual load, whereas a driver who is not heavily stressed tends to display a balanced gaze and stabilization behavior. Furthermore, hectic pedal movements or steering wheel inputs can indicate a high cognitive load.

By using several different test drivers, it is also possible to use the to be able to better differentiate between different user type classes.

A first independent subject relates to a device for controlling a man-machine interface of a motor vehicle, the man-machine interface having at least one display device for displaying information, with means for determining at least one driving situation parameter that represents a driving situation Characterized motor vehicle, are provided and wherein means are provided for determining a load parameter as a function of the determined Fahrsituationspa parameters by means of an algorithm, the load parameter characterizing a cogni tive load of a driver of the motor vehicle, with means for adapting an information density of the at least one display device by means of the display device to be shown information is provided as a function of the load parameter, wherein the algorithm comprises a data set with driving situation parameters, each driving situation parameter a laser tparameter is assigned, the load parameters being based on at least one measured variable that characterize the behavior of the driver.

In a first further refinement, it can be provided that means for determining at least one behavior parameter are also provided, the behavior parameter characterizing a current behavior of the driver, the means for adapting an information density being set up to determine the information density of the information to be displayed on the display device to vary taking into account the behavior parameters determined.

In a further refinement, provision can be made for predictors to be provided for calculating the driver's cognitive load at a predetermined time, in a predetermined period of time and / or at a predetermined distance.

In a further refinement, it can be provided that sensors for detecting measured variables relating to the driver in a normal ferry operation of the motor vehicle and categorization means are provided in order to categorize the driver into one of several classes, the device being set up to display the display configuration in Depending on the user type class of the driver.

In a further refinement, it can be provided that the driving situation-related input variables are parameters relating to the driver, the vehicle, the environment, for vehicle dynamics, road layout, traffic conditions and / or weather.

In a further refinement, it can be provided that at least two display configurations are provided which have a different information density, the means for adapting an information density being set up to adapt the information density by selecting a display configuration.

In a further refinement, it can be provided that the man-machine interface has at least two display devices, on each of which a plurality of information relevant to a driver of the motor vehicle can be displayed, the algorithm being set up to determine which information is on which display device is displayed.

In a further refinement, it can be provided that the algorithm is trained using a machine learning method in that test drives are carried out with a plurality of test drivers, with characteristic parameters for describing a driving situation being recorded by means of recording means during the test drives and in a Memory are stored, the test drivers driving during the test receiving input prompts to an input element of a man-machine interface by means of communication means, an input duration for performing the input being recorded by means of a clock and being correlated with the characteristic parameters by the algorithm.

In a further refinement, it can be provided that acquisition means are provided for acquiring further measurement variables characteristic of the cognitive load of the test driver by means of acquisition means during the test drives and computing means for correlation with the characteristic parameters of the driving situation, the characteristic measurement variables relating to a vehicle position a lane, a number of times the lane has been crossed, a duration of the lane crossing, a deviation from a target speed, head movements, arm movements, steering wheel inputs, pedal inputs, and / or a direction in which the driver is looking.

Another independent subject matter relates to a computer program product with a computer-readable storage medium on which instructions are embedded which, when they are executed by at least one computing unit, cause the at least one Computing unit is set up to carry out the method according to one of the preceding claims.

The method can be carried out on one or more processing units distributed so that certain method steps are carried out on one processing unit and other process steps are carried out on at least one other processing unit, with calculated data being able to be transmitted between the processing units if necessary.

Another independent subject relates to a human-machine interface with at least one display device and at least one arithmetic unit and at least one storage medium connected to the arithmetic unit, on which a computer program product of the type described above is stored, which can be executed by the arithmetic unit.

When used in a motor vehicle, a corresponding human-machine interface can have the aforementioned properties and advantages.

Another independent subject relates to a human-machine interface of a motor vehicle, with at least one display device for the visual display of information relevant to a driver of the motor vehicle, at least one computing unit being provided which is connected to at least one storage medium, where the computing unit for controlling the at least one display device is provided, with at least two different display configurations for operating the at least one display device being stored on the storage medium, where the computing unit is connected to at least one driving situation sensor that detects measured variables relating to a driving situation, with the computing unit for this purpose is set up to determine at least one driving situation parameter from the input variables of the driving situation sensor by means of an algorithm, which characterizes a driving situation of the motor vehicle, and as a function of the ermi ttelten driving situation parameter to determine a load parameter that characterizes a cognitive load of a driver of the motor vehicle, and depending on the load parameter, to select one of the display configurations and to display it using the at least one display device, the algorithm comprising a data set with driving situation parameters, each driving situation parameter being assigned a load parameter is, the load parameters on Based on at least one measured variable that characterize a behavior of the driver.

A corresponding driving situation sensor can be, for example, a camera, a radar system, a motor vehicle-to-motor vehicle communication device, a motor vehicle-to-vehicle communication device, a navigation system and / or a weather sensor such as a temperature and / or rain sensor.

The algorithm can be a map-based algorithm, for example.

In a first further refinement, it can be provided that the computing unit is connected to at least one driver monitoring sensor that detects measured variables relating to a driver, the computing unit being designed to calculate the load parameter using the measured variables of the driver monitoring sensor.

A corresponding driver monitoring sensor can be, for example, a camera that is aimed at the driver and detects his head movement, gestures and gaze parameters such as eyelid closure times, gaze duration, gaze direction and / or eye movements, etc. Another driver monitoring sensor can be a pure eye sensor.

In a further refinement, it can be provided that the computing unit is designed to monitor an interaction of the driver with the man-machine interface and is designed to record measured variables describing the interaction.

This allows input actions, e.g. operating time, operating errors, interruptions in operation, to be monitored and used for the display configuration.

In a further refinement, it can be provided that the computing unit is connected to at least one vehicle sensor which detects measured variables relating to a driving state of the motor vehicle.

Such a sensor can, for example, be a pedal sensor for detecting pedal inputs, a steering wheel sensor for detecting steering inputs, a speed sensor, be an acceleration sensor for detecting an acceleration and / or a cornering speed and / or a slip sensor.

In a further refinement, it can be provided that at least two display devices are provided, the computing unit being set up to use the calculated cognitive load to select on which of the at least two display devices information relevant to the driver is displayed.

Such display devices can be, for example, a head-up display, a central information display and / or a cockpit display.

In a further refinement, it can be provided that the algorithm stored on the storage medium is a self-learning algorithm.

Self-learning algorithms are able to process very complex situations.

Depending on the design, the algorithm can also have artificial intelligence.

Another independent subject matter relates to a motor vehicle with a human-machine interface of the type described above.

Further features and details emerge from the following description in which - if necessary with reference to the drawing - at least one Ausführungsbei game is described in detail. Described and / or graphically represented features form the subject matter individually or in any meaningful combination, possibly also independently of the claims, and in particular can also be the subject matter of one or more separate applications. Identical, similar and / or functionally identical parts are provided with the same reference numerals. They show schematically:

1 shows a motor vehicle with a human-machine interface;

2A, B show display devices of the human-machine interface parts from FIG. 1 in two different configurations;

FIG. 3 shows a driving situation in which the motor vehicle from FIG. 1 is located; FIG. 4A, B are basic sketches for training an algorithm of the man-machine interface from FIG. 1, as well as

5 shows a schematic diagram of the decision architecture of the man-machine

Cut parts.

1 shows a motor vehicle 2 with a human-machine interface 4 (components framed by dashed lines).

The human-machine interface 4 is an interface between the motor vehicle 2 and a driver 6 and possibly other passengers. By means of the human-machine interface 4, the driver 6 can receive information from and about the motor vehicle 2 and can make control inputs for the motor vehicle 4 via suitable input means (not shown), e.g. for navigation, multimedia control, air conditioning, lighting, etc.

In the present case, the human-machine interface 4 has three displays, a head-up display 8, a cockpit display 10 and a central information display 12. The head-up display 8 displays information on a windshield of the motor vehicle 2, which Cock pit display 10 is net angeord in front of a (not shown) steering wheel directly in front of the driver 6 and the central information display 12 near the center of a (not shown in Fig. 1) dashboard.

The man-machine interface 4 has a controller 14 with a computing unit 16 and a storage medium 18 on which an algorithm 20 is stored, which will be explained in detail below.

The controller 14 is connected to a multiplicity of sensors, of which a radar sensor 22, a steering angle sensor 24, a traffic camera 26 and a driver monitoring camera 28 are shown by way of example in FIG. 1. The controller 14 can be connected to a number of additional sensors. In addition to the radar sensors 22 and the traffic camera 26, a first group of such sensors can be further sensors for traffic monitoring such as lidar or communication devices such as vehicle-to-vehicle communication means or vehicle-to-infrastructure communication means, navigation systems, weather sensors and the like, all of which serve to obtain information about the traffic situation.

In addition to the steering angle sensor 24, a second group of corresponding sensors can be sensors for monitoring the condition of the motor vehicle, for example speed sensors, wheel speed sensors, drive sensors, pedal sensors, etc.

For the third group of driver monitoring sensors, in addition to the driver monitoring camera 28, drowsiness sensors can be used, but the pedal sensors and steering angle sensor 24 already mentioned in connection with the second group can be used for driver monitoring, since this enables driver actions on controls of the motor vehicle 2, e.g. steering wheel, accelerator - or brake pedal, are detectable.

Radar sensors 22 and traffic cameras 26 are used to monitor the traffic in front of the motor vehicle 2. With the aid of this information, the controller 14 can use the algorithm 20 to make predictions about the current and future traffic situation and situation.

With the help of the steering angle sensor 24 and other sensors for monitoring the motor vehicle 2, the controller 14 can obtain information about the dynamic state of the motor vehicle 2.

With the aid of the driver monitoring camera 28, it is possible to determine how busy or how distracted the driver 6 is at the moment. The driver monitoring camera 28 can, for example, determine movements and alignment of a head 30 of the driver 6 and / or that of his eyes. Such information can allow conclusions about the cognitive load of the driver of the motor vehicle 2 in addition to the monitoring of the control inputs in the motor vehicle 2 by means of the steering wheel and pedals.

2A, B show the display devices 8, 10, 12 of the man-machine interface 4 from FIG. 1 in two different display configurations A and B. The display devices 8, 10, 12 are arranged on a dashboard 32.

In the configuration shown in FIG. 2A, the head-up display 8 displays information on the current driving speed 34, navigation information 36 and lane departure warning information 38 and thus provides the driver with comprehensive information.

The cockpit display 10 also shows information on the current driving speed 34 as well as speed information 40. In addition, multimedia information 42 is presented.

Map information 44 is displayed on the central information display 12.

2A shows a display configuration A in which the driver's cognitive load is low and he can receive and process a large amount of information from the human-machine interface 4.

In the display configuration B according to FIG. 2B, on the other hand, the cognitive load is higher and a very reduced amount of information is displayed compared to the display configuration according to FIG. 2A.

Thus, only the navigation information 36 is shown on the head-up display 8. Only the driving speed information 34 is shown on the cockpit display 10.

In addition to the map information 44, the multimedia information 42 is also shown on the central information display 12.

The configuration shown in FIG. 2B allows the driver to quickly grasp the information that is most relevant in the corresponding driving situation.

FIG. 3 shows a driving situation in which the motor vehicle 2 from FIG. 1 is located.

The motor vehicle 2 is approaching an intersection 46. There is a lot of traffic at intersection 46. Another motor vehicle 48 drives in front of the motor vehicle 2, the motor vehicle 2 is approached by a further motor vehicle 50, the intersection 46 is crossed by another motor vehicle 52, an oncoming motor vehicle 54 is waiting, and a pedestrian 56 runs across a zebra crossing 58.

At the time of the display, the cognitive load of the driver 6 of the motor vehicle 2 is only slightly increased, since he is preparing for the navigation at the upcoming intersection 46. The closer he comes to the intersection 46, the higher the cognitive load becomes, since on the one hand he has to monitor the behavior of the large number of road users 48 to 56, on the other hand he still has to navigate. In this case, it is difficult for the driver 6 to record all the available information pertaining to the navigation system and the motor vehicle 2, which is why it makes sense to considerably reduce the number of information displayed on the human-machine interface 4 and to reduce the most important information accordingly large and concise, so that they catch the eye.

FIGS. 4A, B show basic sketches for training the algorithm 20 of the human-machine interface parts 4 from FIG. 1.

The algorithm 20 has two different dimensions of classification, on the one hand relating to the current driving situation and on the other hand relating to the driver. Algorithm 20 is a self-learning algorithm that is developed through training with test drivers on test drives.

During the training of the algorithm 20, characteristic parameters are recorded and evaluated by means of a data recorder in order to describe a driving situation. Furthermore, other measured variables characteristic of driver distraction, such as eye or head movements and viewing direction, gestures and / or pedal and steering angle settings, can be recorded. Furthermore, it can be recorded how long it takes the driver to make a steering input, where he is in the lane, under what circumstances, how long and how often he leaves the lane and how constant the speed is.

During the aforementioned test drives, the test drivers are repeatedly requested to make one or more standardized operator inputs on the human-machine interface 4. Different traffic conditions prevail during operator input. The input time that a test driver needs for the required input, correlates on the one hand with the cognitive load of the driver due to the driving situation and on the other hand with the driver's distraction, for example due to the request for information.

The measured operating times for completing the tasks are recorded and evaluated together with the characteristic parameters.

4A shows how different drivers are sorted into different driver type classes based on the input variables described above for a current situation (vehicle dynamics, route, traffic, weather, human-machine interface inputs).

Experienced drivers will have fewer lane deviations in comparable driving situations and will be able to make inputs into the human-machine interface parts 4 more quickly than less experienced drivers. The latter should be less stressed by the human-machine interface 4 than the former, e.g. through a simplified menu navigation. Such drivers are then sorted into a lower driver type class than experienced drivers.

4B shows how an analysis of the driver's cognitive load as a function of the current position, the current route section, the traffic, the weather, etc. is carried out on the basis of the same input variables.

Thus, on the one hand, a preselection of the display configurations A, B in question can take place and, on the other hand, a correlation between the driving situation and the cognitive load can be made. From this, the algorithm 20 used in the motor vehicle 2 can be developed, which decides on the information displayed on the displays 8, 10, 12 of the human-machine interface parts 4.

FIG. 5 shows a schematic diagram of the decision architecture of the human-machine interface parts 4.

In the later ferry operation, the same input variables as during training according to FIGS. 4A, B are recorded and evaluated by means of the artificial intelligence algorithm 20. In addition to the current inputs, a so-called digital electronic horizon is created, i.e. a forecast of the driving situation that will shortly prevail. The horizon can cover a distance of, for example, 50 to 200 m or 1000 m or a period of, for example, 5 to 30 seconds. The corresponding input variables can be dynamically predicted via the digital electronic horizon (e.g. using ADASIS).

On the one hand, the algorithm 20 uses the input variables to calculate which driver type class the current driver belongs to. On the other hand, the algorithm 20 makes a prediction of the cognitive load that will prevail in the coming route section. This information is transferred to the controller 14 of the man-machine interface 4, which selects a corresponding display configuration A, B therefrom.

Depending on the user type, e.g. three cognitive performance levels (3 high - 2 medium - 1 low) could be mapped on e.g. five display levels (4 max - 3 high - 2 medium - 1 low - 0 minimal). For a driver with performance level 3 high, load levels 3-2-1 can be assigned to display levels 4-3-2, for a driver with performance level 2, display levels 3-2-1, and a driver with performance level 1, display levels 2 -1-0.

In alternative refinements, instead of artificial intelligence, a map can be created according to which a corresponding display configuration A, B is selected as a function of the input variables.

Although the subject matter has been illustrated and explained in more detail by means of exemplary embodiments, the invention is not restricted by the examples disclosed and other variations can be derived therefrom by the person skilled in the art. It is therefore clear that there is a multitude of possible variations. It is also clear that the exemplary embodiments mentioned are only examples that are not to be understood in any way as a limitation, for example, of the scope of protection, the possible applications or the configuration of the invention. Rather, the preceding description and the description of the figures enable the person skilled in the art to actually implement the exemplary embodiments, with the person skilled in the art knowing the inventive concept disclosed various changes, for example with regard to the function or the arrangement of individual elements mentioned in an exemplary embodiment without leaving the scope of protection that is defined by the claims and their legal equivalents, such as further explanation in the description.

List of reference symbols

2 motor vehicle

4 man-machine cut parts

6 drivers

8 head-up display

10 cockpit display

12 central information display

14 Control

16 arithmetic unit

18 Storage medium

20 algorithm

22 radar sensor

24 steering angle sensor

26 traffic camera

28 Driver surveillance camera

30 head

32 dashboard

34 Driving speed information. 36 Navigation information

38 Lane Keeping Assist information

40 Speed information

42 Multimedia information

44 Map Information

46 intersection

48, 50, 52, 54 motor vehicle 56 pedestrians

58 zebra crossing

A, B display configuration

Claims

1. A method for controlling a human-machine interface (4) of a motor vehicle (2), the human-machine interface (4) having at least one display device (8, 10, 12) for displaying information (34, 36 , 38, 40, 42, 44), with at least one driving situation parameter being determined which characterizes a driving situation of the motor vehicle (2), and with an algorithm (20) determining a load parameter which is a cognitive Load of a driver (6) of the motor vehicle (2) characterized so that an information density of the at least one display device (8, 10, 12) of information to be displayed by means of the display device (8, 10, 12) is adapted as a function of the load parameter, the Algorithm (20) comprises a data record with driving situation parameters, each driving situation parameter being assigned a load parameter, the load parameters being based on are based on at least one measured variable that characterize a behavior of the driver.

2. The method according to claim 1, wherein a behavior parameter is also determined which characterizes a current behavior of the driver, the information density of the information to be displayed on the display device (8, 10, 12) being varied taking into account the behavior parameters determined.

3. The method according to claim 1 or 2, wherein the calculation of the parameter includes a prediction of the cognitive load of the driver (6) at a predetermined time, in a predetermined period of time and / or a predetermined distance.

4. The method according to any one of the preceding claims, wherein in a normal ferry operation of the motor vehicle (2) the driver (4) related measured variables are recorded and the driver (4) is categorized into one of several classes, the information density depending on the user type class of the driver (6) is adjusted.

5. The method according to any one of the preceding claims, wherein the driving situation related input variables include measured variables of the environment, driver (6) and / or motor vehicle (2).

6. The method according to any one of the preceding claims, wherein at least two display configurations (A, B) are provided which have a different information density, the information density being adapted by selecting a display configuration (A, B).

7. The method according to any one of the preceding claims, wherein the man-machine interface (4) has at least two display devices (8, 10, 12), on each of which a plurality of for a driver (4) of the motor vehicle (2) Relevant information (34, 36, 38, 40, 42, 44) can be displayed, with the algorithm (20) determining which information (34, 36, 38, 40, 42, 44) is on which display device (8 , 10, 12) is displayed.

8. The method according to any one of the preceding claims, wherein the algorithm (20) is trained by a machine learning method in that test drives are carried out with a plurality of test drivers, with characteristic parameters being recorded and stored during the test driving th for describing a driving situation , the test drivers receiving input requests to an input element of a man-machine interface during the test drives, an input duration for performing the input, an input error rate and / or a number of operating steps being recorded and correlated with the characteristic parameters by the algorithm (20) become.

9. The method according to any one of the preceding claims, wherein during the test drives, further measured variables characteristic of the cognitive load of the test driver (4) are recorded and correlated with the characteristic parameters of the driving situation, the characteristic measured variables being a vehicle position in a lane, a number of crossing the lane, a duration of the lane crossing, a deviation from a target speed, head movements, arm movements, steering wheel inputs, pedal inputs, and / or a direction in which the driver is looking.

10. Computer program product, with a computer-readable storage medium (18) the commands are embedded which, when they are executed by at least one processing unit (16), have the effect that the at least one processing unit (16) is set up to carry out the method according to one of the preceding claims.

11. Human-machine interface parts (4) with at least one display device (8, 10, 12) and at least one computing unit (16) and at least one storage medium (18) connected to the computing unit (16), on which a computer program product after Claim 10 is stored, which can be executed by the computing unit (16).

12. Human-machine interface parts of a motor vehicle (2), with at least one display device for the visual display of information relevant to a driver (4) of the motor vehicle (2), with at least one computing unit (16) being seen with at least one storage medium (18) is connected, the computing unit (16) being provided for controlling the at least one display device (8, 10, 12), with at least two different display configurations (A, B) for operation on the storage medium (18) the at least one display device (8, 10, 12) are stored, the computing unit (16) being connected to at least one driving situation sensor (22, 26) which detects at least one measured variable relating to the driving situation, the computing unit (16) being set up to do so is to determine from the input variables of the driving situation sensor (22, 26) by means of an algorithm (20) at least one driving situation parameter that a driving situation of the Kr aftfahrzeuges (2), and depending on the determined driving situation parameter, to determine a load parameter that characterizes a cognitive load of a driver (6) of the motor vehicle (2) and, depending on the load parameter, to select one of the display configurations (A, B) and use the at least one display device (8, 10, 12), the algorithm (20) comprising a data set with driving situation parameters, each driving situation parameter being assigned a load parameter, the load parameters being based on at least one measured variable that characterize the behavior of the driver .

13. Man-machine interface parts according to claim 12, wherein the computing unit (16) is connected to at least one driver monitoring sensor (28) which is a driver (4) records the relevant measured variables, the computing unit (16) being designed to calculate the load parameter with the aid of the measured variables from the driver monitoring sensor (28).

14. Man-machine interface parts according to claim 13, wherein the computing unit (16) is designed to monitor an interaction of the driver (6) with the man-machine interface (4) and is designed to record measured variables describing the interaction .

15. Motor vehicle with a man-machine interface (4) according to one of claims 11 to 14.