DE102019217346A1 - Method for displaying information on a man-machine interface of a motor vehicle, computer program product, man-machine interface and motor vehicle - Google Patents

Abstract

A method is described for controlling a man-machine interface (4) of a motor vehicle (2), the man-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 that represents a cognitive load of a The driver (6) of the motor vehicle (2) is 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 set with driving situation parameters, each driving situation parameter being assigned a load parameter, the load parameters it is based on at least one measured variable that characterizes the behavior of the driver. A computer program product, human-machine interfaces and a motor vehicle are also described.

Description

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

Methods for displaying information on a man-machine interface of a motor vehicle, computer program products, man-machine interfaces 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 that 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. Less attention is required on an easy route with little traffic 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 man-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 to use different display modes during the day and at night, for example a night mode on a display. It is also known to vary the color of the display. It is also known to configure the display as a function of the driving mode, for example in sports cars differently for road operation and for racing track operation. In addition, there are 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 the DE 10 2007 058 437 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's view by means of a head-up display, the overall display of the head-up display comprising a group of adjacent display fields in which information displays can be displayed , wherein each element of a set of possible information displays for display is assigned to one of the display fields, with characteristic driving situation parameters of the current driving situation being determined, with the information displays assigned to this display field being prioritized for each display field depending on the characteristic driving situation parameters and with the content of each display field in Depending on the prioritization made for this display field.

Although the driving situation parameters known from the prior art can sometimes correlate coincidentally with the driver's cognitive load, 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 man-machine interface of a motor vehicle, computer program products, man-machine interfaces and motor vehicles of the type mentioned above so that the cognitive load of a driver can be determined more directly and thus more precisely and the human-machine interface can be configured 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 independent claim 12 and a motor vehicle according to independent claim 15. Further refinements and developments are the subject of the dependent claims.

A method for controlling a human-machine interface of a motor vehicle is described below, the human-machine Machine interface has at least one display device for displaying information, with at least one driving situation parameter being determined that characterizes a driving situation of the motor vehicle, and with an algorithm determining a load parameter that characterizes a cognitive load of a driver of the motor vehicle as a function of the determined driving situation parameter So that an information density of the at least one display device of information to be displayed by means of the display device is adapted as a function of the load parameter, the algorithm comprising a data record with driving situation parameters, each driving situation parameter being assigned a load parameter, the load parameters being based on at least one measured variable, which characterize a behavior of the 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 due to 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, the traffic is driving 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. As a result, the information density can be better adapted 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 predetermined time, in a predetermined time span and / or a predetermined distance.

This allows a prediction to be made about the cognitive load to be expected shortly and a suitable display configuration to 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 measured variables relating to the driver are recorded during normal driving of the motor vehicle 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, for example age-related, differences in the capacity and processing speed of 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 refinement, as a result of which a display configuration tailored to the driver can be selected.

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 as 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 likely to be a cognitive burden on the driver to predict. 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, which in some configurations can differ in more than one parameter dimension. For example, 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 at a certain location, for example a speed display, a speed, a range and like that. Thus, configurations 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, 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 and stored during the test drives, with the test drivers during of the test drives receive input requests to an input element of a man-machine interface, 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.

Using machine learning methods, it is possible to reliably assess very complex situations and, in particular, to make predictions about future cognitive loads as a function of a prediction of the various driving situations. Such predictions are difficult to make with classically structured algorithms. Furthermore, algorithms of this type can be continuously improved during ongoing operation, so that the prediction of the cognitive load and, depending on the design, the categorization of the drivers 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 cognitive load of the test driver 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 within 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.

Monitoring the driver himself, for example by means of an interior camera, can also 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 better differentiate the different user type classes from one another.

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 characterizes a driving situation of the motor vehicle, are provided and means are provided for determining a load parameter as a function of the determined driving situation parameter by means of an algorithm, the load parameter characterizing a cognitive load of a driver of the motor vehicle, with means for adapting an information density of the at least one display device as a function of information to be displayed by means of the display device of the load parameter are provided, the algorithm comprising a data record with driving situation parameters, each driving situation parameter having a load parameter it is assigned, the load parameters being based on at least one measured variable which characterize a behavior of the driver.

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

In a further refinement, it can be provided that prediction means are provided for calculating the driver's cognitive load at a given time, in a given time span and / or a given distance.

In a further refinement, it can be provided that sensors are provided for detecting measured variables relating to the driver in normal driving mode 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 include parameters relating to the driver, the vehicle, the environment, the vehicle dynamics, road layout, the traffic condition and / or the 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 by 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 and stored in a memory during the test drives During the test drives, the test drivers receive 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 measured variables characteristic of the cognitive load of the test drivers by acquisition means during the test drives and computing means for correlation with the characteristic parameters of the driving situation, the characteristic measured variables being a vehicle position in a lane, a number of Exceeding 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 of view of the driver include.

Another independent subject matter relates to a computer program product with a computer-readable storage medium on which instructions are embedded which, when executed by at least one processing unit, have the effect that the at least one processing 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, so that certain method steps are carried out on one processing unit and other method steps are carried out on at least one further processing unit, with calculated data being able to be transmitted between the processing units if necessary.

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

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

A further independent subject relates to a man-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, the computing unit for control 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, the computing unit being connected to at least one driving situation sensor that detects measured variables relating to a driving situation, the computing unit being set up to use the To determine input variables of the driving situation sensor by means of an algorithm at least one driving situation parameter which characterizes a driving situation of the motor vehicle, and as a function of the determined 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 by means of 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, wherein the load parameters are based on at least one measured variable that characterizes 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 acquire 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 Speed sensor, 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 relates to a motor vehicle with a man-machine interface of the type described above.

• 1 ein Kraftfahrzeug mit einer Mensch-Maschine-Schnittstelle;
• 2A, B Anzeigevorrichtungen der Mensch-Maschine-Schnittstelle aus 1 in zwei unterschiedlichen Konfigurationen;
• 3 eine Fahrsituation, in der sich das Kraftfahrzeug aus 1 befindet;
• 4A, B Prinzipskizzen zum Training eines Algorithmus der Mensch-Maschine-Schnittstelle aus 1, sowie
• 5 eine Prinzipskizze der Entscheidungsarchitektur der Mensch-Maschine-Schnittstelle.

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

• 1 a motor vehicle with a man-machine interface;
• 2A, B Display devices of the man-machine interface 1 in two different configurations;
• 3rd a driving situation in which the motor vehicle is from 1 is located;
• 4A, B Outline sketches for training an algorithm of the human-machine interface 1 , as
• 5 a schematic diagram of the decision architecture of the human-machine interface.

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

The human-machine interface 4th is an interface between the motor vehicle 2 and a driver 6th as well as other passengers, if applicable. Using the human-machine interface 4th can the driver 6th Information from and about the motor vehicle 2 received as well as via suitable, not shown, input means control inputs for the motor vehicle 4th perform, e.g. for navigation, multimedia control, air conditioning, lighting etc ..

In the present case, the human-machine interface 4th three displays, one head-up display 8th , a cockpit display 10 as well as a central information display 12th . The head-up display 8th fades information into a windshield of the motor vehicle 2 one, the cockpit display 10 is in front of a (not shown) steering wheel directly in front of the driver 6th arranged and the central information display 12th near the center of a (in 1 not shown) dashboard.

The human-machine interface 4th instructs a controller 14th with one arithmetic unit 16 and a storage medium 18th on which an algorithm 20th is stored, which will be explained in detail below.

The control 14th is connected to a large number of sensors, of which, for example, in 1 a radar sensor 22nd , a steering angle sensor 24 , a traffic camera 26th as well as a driver monitoring camera 28 are shown. The control 14th can be connected to a number of other sensors.

A first group of such sensors can be in addition to the radar sensors 22nd and the traffic camera 26th further sensors for traffic monitoring such as lidar or communication devices such as motor vehicle-to-motor vehicle communication means or motor vehicle-to-infrastructure communication means, navigation systems, weather sensors and the like, all of which are used to obtain information about the traffic situation.

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

The third group of driver monitoring sensors can be added to the driver monitoring camera 28 Fatigue sensors serve, but so do the pedal sensors and the steering angle sensor already mentioned in connection with the second group 24 can be used for driver monitoring, since this is used by driver actions on the control means of the motor vehicle 2 , for example steering wheel, accelerator or brake pedal, can be detected.

Radar sensors 22nd as well as traffic camera 26th are used to monitor yourself in front of the motor vehicle 2 existing traffic. With the help of this information, the controller 14th using the algorithm 20th 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 can control 14th Information about the dynamic state of the motor vehicle 2 win.

With the help of the driver monitoring camera 28 it is possible to determine how busy or how distracted the driver is 6th currently is. The driver monitoring camera 28 for example, movements and alignment of a head 30th of the driver 6th and / or his eyes. Such information can be used in addition to monitoring the control inputs in the motor vehicle 2 Using the steering wheel and pedals to draw conclusions about the cognitive load of the driver of the motor vehicle 2 allow.

2A, B show the display devices the 8th , 10 , 12th the human-machine interface 4th out 1 in two different display configurations A. and B. .

The display devices 8th , 10 , 12th are on a dashboard 32 arranged.

The head-up display 8th shows in the in 2A the configuration shown, information on the current driving speed 34 , Navigation information 36 as well as lane departure warning information 38 and thus informs the driver comprehensively.

The cockpit display 10 also shows information about the current driving speed 34 as well as speed information 40 at. In addition, multimedia information 42 shown.

On the central information display 12th are map information 44 displayed.

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

In the display configuration B. according to 2 B on the other hand, the cognitive load is higher and it becomes one in accordance with the display configuration 2 A very reduced amount of information presented.

So will be on the head-up display 8th only the navigation information 36 shown. On the cockpit display 10 is only the driving speed information 34 shown.

On the central information display 12th are next to the card information 44 also the multimedia information 42 shown.

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

3rd shows a driving situation in which the motor vehicle is 2 out 1 is located.

The car 2 drives to an intersection 46 to. At the intersection 46 there is a lot of traffic. In front of the motor vehicle 2 drives another motor vehicle 48 , the motor vehicle 2 comes another motor vehicle 50 opposite, the intersection 46 is from another motor vehicle 52 crossed an oncoming motor vehicle 54 waiting and a pedestrian 56 runs across a zebra crossing 58 .

At the time of presentation is the driver's cognitive load 6th of the motor vehicle 2 only increased slightly as he focused on navigation at the upcoming intersection 46 prepared. The closer he is to the intersection 46 comes, the higher the cognitive load, since on the one hand it affects the behavior of the large number of road users 48 to 56 has to monitor, on the other hand still has to navigate. In this case, it falls to the driver 6th difficult to find all available information regarding navigation and motor vehicle 2 which is why it makes sense to count the amount of information displayed on the human-machine interface 4th to reduce significantly and to present the most important information in a large and concise manner so that it catches the eye.

4A, B show basic sketches for training the algorithm 20th the human-machine interface 4th out 1 .

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

During the training of the algorithm 20th characteristic parameters are recorded and evaluated by means of a data recorder 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 is possible to record 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 asked to provide one or more standardized operator inputs at the human-machine interface 4th to undertake. 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 driver's cognitive load 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.

In 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, man-machine interface inputs).

Experienced drivers will have fewer lane deviations in comparable driving situations and input into the human-machine interface more quickly 4th than less experienced drivers. The latter should be from the man-machine interface 4th are less stressed than the former, for example through a simplified menu navigation. Such drivers are then sorted into a lower driver type class than experienced drivers.

In 4B shows how an analysis of the driver's cognitive load depending on 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 that come into question can be made A. , B. and, on the other hand, a correlation is made between the driving situation and the cognitive load. From this the in the motor vehicle 2 used algorithm 20th developed based on the information displayed on the advertisements 8th , 10 , 12th the human-machine interface 4th decides.

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

In later driving, the same input variables are used as in training according to 4A, B recorded and using the artificial intelligence algorithm 20th evaluated.

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 encompass 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).

The algorithm calculates from the input variables 20th on the one hand, which driver type class the current driver belongs to. On the other hand, the algorithm hits 20th a prediction of the cognitive load that will prevail in the coming section of the route. This information is sent to the controller 14th the human-machine interface 4th passed, which from it a corresponding display configuration A. , B. selects.

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 the display levels 3-2-1 and a driver with performance level 1 the display levels 2-1 -0.

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

Although the subject matter has been illustrated and explained in more detail by means of exemplary embodiments, the invention is not restricted by the disclosed examples 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 exemplary embodiments cited are only examples that are not in any way intended to limit the scope of protection to which Possible applications or the configuration of the invention are to be understood. Rather, the preceding description and the description of the figures enable the person skilled in the art to specifically implement the exemplary embodiments, whereby the person skilled in the art, with knowledge of the disclosed inventive concept, can make various changes, for example with regard to the function or the arrangement of individual elements mentioned in an exemplary embodiment, without the To leave 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

4th

Man-machine interface

6th

driver

8th

Head-Up Display

10

Cockpit display

12th

central information display

14th

control

16

Arithmetic unit

18th

Storage medium

20th

algorithm

22nd

Radar sensor

24

Steering angle sensor

26th

Traffic camera

28

Driver surveillance camera

30th

head

32

dashboard

34

Driving speed information

36

Navigation information

38

Lane Keeping Assist information

40

Speed information

42

Multimedia information

44

Card information

46

Intersection

48, 50, 52, 54

Motor vehicle

56

pedestrian

58

Zebra crossing

A, B

Display configuration

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

• DE 102007058437 A1 [0006]

Claims (15)

Method for controlling a man-machine interface (4) of a motor vehicle (2), the man-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 as a function of the determined driving situation parameter, which is a cognitive load of a driver (6 ) of the motor vehicle (2) 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) generating a data set with driving situation parameters, each driving situation parameter being assigned a load parameter, the load parameters being based on at least a few ns based on a measured variable that characterize a behavior of the driver. Procedure according to Claim 1 Furthermore, a behavior parameter is 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 determined behavior parameter. Procedure according to Claim 1 or 2 , wherein for calculating the parameter includes a prediction of the cognitive load of the driver (6) at a given time, in a given time span and / or a given distance. Method according to one of the preceding claims, wherein in normal driving operation of the motor vehicle (2) the driver (4) pertaining 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. Method according to 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). Method according to 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). Method according to 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 items of information (34 , 36, 38, 40, 42, 44) can be displayed, with the algorithm (20) determining which information (34, 36, 38, 40, 42, 44) is displayed on which display device (8, 10, 12) becomes. Method according to 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 for describing a driving situation being recorded and stored during the test drives, the test drivers receive 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). Method according to 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 times it is exceeded 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. Computer program product with a computer-readable storage medium (18) on which instructions are embedded which, when executed by at least one computing unit (16), have the effect that the at least one computing unit (16) is set up to implement the method according to one of the preceding To carry out claims. Man-machine interface (4) with at least one display device (8, 10, 12) and at least one computing unit (16) as well as at least one storage medium (18) connected to the computing unit (16) on which a computer program product is stored Claim 10 is stored, which can be executed by the computing unit (16). Man-machine interface 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), at least one computing unit (16) being provided which is equipped with at least one storage medium (18), 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 operating the at least one display device on the storage medium (18) (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 use the input variables of the Driving situation sensor (22, 26) using an algorithm (20) to determine at least one driving situation parameter that characterizes a driving situation of the motor vehicle (2) and, depending on the determined driving situation parameter, to determine a load parameter that represents a cognitive load of a driver (6) of the motor vehicle (2) characterized, and depending on the load parameter, one of the display configurations (A, B) to be selected u nd to be displayed by means of the at least one display device (8, 10, 12), the algorithm (20) comprising a data record 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 indicates a behavior of the Characterize the driver. Man-machine interface Claim 12 , the computing unit (16) being connected to at least one driver monitoring sensor (28) which detects measured variables relating to a driver (4), the computing unit (16) being designed to calculate the load parameter using the measured variables of the driver monitoring sensor (28). Man-machine interface 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. Motor vehicle with a man-machine interface (4) according to one of the Claims 11 to 14th .

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