METHOD FOR DYNAMICALLY ADAPTING AN OPERATING DEVICE IN A MOTOR VEHICLE, AS WELL AS OPERATING DEVICE AND MOTOR VEHICLE

Abstract

A method for adapting an operating device in a motor vehicle. The operating device is provided for operating at least one vehicle function of the motor vehicle. In at least one driving situation, observation data which describe the current driving situation and usage data which describe which of the vehicle functions is currently activated via the operating device are in each case detected by the operating device, and an assignment rule is generated on the basis of the observation data and the usage data by a procedure of automatic learning.

Claims

1. A method for adapting an operating device in a motor vehicle, comprising: the operating device being provided for operating at least one motor vehicle function of the motor vehicle and, in the operating device in an adapted state, a number of displayed control elements for respectively activating one of the motor vehicle functions being reduced in comparison with a predetermined, unadapted state, and/or a presetting of at least one function parameter of at least one of the motor vehicle functions being set differently from a predetermined standard setting, wherein in at least one driving situation, observation data which describe the current driving situation and usage data which describe which of the vehicle functions is currently activated and/or which respective setting is present for the at least one function parameter are in each case detected by the operating device, in at least one driving situation, and, using the observation data and the usage data, an assignment rule is generated by means of a procedure of machine learning by the operating device, which procedure assigns to the observation data a state of the operating device adapted in accordance with the usage data, and in at least one additional driving situation, in each case the state of the operating device is adapted by means of the assignment rule as a function of observation data determined in the additional driving situation by the operating device.

2. The method of claim 1, wherein during adapting the state of the operating device to the additional driving situation by the assignment rule, in at least one operating level of an operating interface of the operating device of relevant operating elements of the operating level, at least one control element for activating such a vehicle function, which is operated with less than a predetermined minimum probability in the respective additional driving situation, is deleted from the operating level.

3. The method of claim 2, whereby each deleted control element is arranged in a predetermined alternate menu level.

4. The method of claim 2, wherein the assignment rule comprises a decision rule which prescribes a predetermined switching grid concerning the possible number of control elements for the respective operating level, and in each case as many control elements are deleted so that the adapted operating level corresponds to the switching grid.

5. The method of claim 2, in which, in the event that only a single control element would remain in an operating level in accordance with the user data with a predetermined minimum probability, in place of a display of the remaining control element, the motor vehicle function, which can be activated by the control element, is activated directly.

6. The method of claim 1, whereby, when adapting the state of the operating device to the additional driving situation, the assignment rule specifies a sorting for at least one list of function parameters.

7. The method of claim 6, whereby at least one list displays telephone numbers and/or navigation destinations and/or radio stations as function parameters.

8. The method of claim 1, whereby the observation data describe at least one of the following characteristics of a driving situation: a time of day of a trip and/or a day of the week of the trip and/or a weather situation and/or temperature during the trip and/or a region through which the trip passes and/or a destination of the trip predetermined in a navigation system and/or estimated by a destination estimate.

9. The method of claim 3, wherein the assignment rule comprises a decision rule which prescribes a predetermined switching grid concerning the possible number of control elements for the respective operating level, and in each case as many control elements are deleted so that the adapted operating level corresponds to the switching grid.

10. The method of claim 3, in which, in the event that only a single control element would remain in an operating level in accordance with the user data with a predetermined minimum probability, in place of a display of the remaining control element, the motor vehicle function, which can be activated by the control element, is activated directly.

11. The method of claim 4, in which, in the event that only a single control element would remain in an operating level in accordance with the user data with a predetermined minimum probability, in place of a display of the remaining control element, the motor vehicle function, which can be activated by the control element, is activated directly.

12. The method of claim 2, whereby, when adapting the state of the operating device to the additional driving situation, the assignment rule specifies a sorting for at least one list of function parameters.

13. The method of claim 3, whereby, when adapting the state of the operating device to the additional driving situation, the assignment rule specifies a sorting for at least one list of function parameters.

14. The method of claim 4, whereby, when adapting the state of the operating device to the additional driving situation, the assignment rule specifies a sorting for at least one list of function parameters.

15. The method of claim 5, whereby, when adapting the state of the operating device to the additional driving situation, the assignment rule specifies a sorting for at least one list of function parameters.

16. The method of claim 2, whereby the observation data describe at least one of the following characteristics of a driving situation: a time of day of a trip and/or a day of the week of the trip and/or a weather situation and/or temperature during the trip and/or a region through which the trip passes and/or a destination of the trip predetermined in a navigation system and/or estimated by a destination estimate.

17. The method of claim 3, whereby the observation data describe at least one of the following characteristics of a driving situation: a time of day of a trip and/or a day of the week of the trip and/or a weather situation and/or temperature during the trip and/or a region through which the trip passes and/or a destination of the trip predetermined in a navigation system and/or estimated by a destination estimate.

18. The method of claim 4, whereby the observation data describe at least one of the following characteristics of a driving situation: a time of day of a trip and/or a day of the week of the trip and/or a weather situation and/or temperature during the trip and/or a region through which the trip passes and/or a destination of the trip predetermined in a navigation system and/or estimated by a destination estimate.

19. The method of claim 5, whereby the observation data describe at least one of the following characteristics of a driving situation: a time of day of a trip and/or a day of the week of the trip and/or a weather situation and/or temperature during the trip and/or a region through which the trip passes and/or a destination of the trip predetermined in a navigation system and/or estimated by a destination estimate.

20. The method of claim 6, whereby the observation data describe at least one of the following characteristics of a driving situation: a time of day of a trip and/or a day of the week of the trip and/or a weather situation and/or temperature during the trip and/or a region through which the trip passes and/or a destination of the trip predetermined in a navigation system and/or estimated by a destination estimate.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The following describes examples of the invention's embodiment. Here,

[0027] FIG. 1 shows a schematic view of an embodiment of the invention-related motor vehicle; and

[0028] FIG. 2 shows a sketch illustrating a menu level of a menu structure of the operating device of FIG. 1.

DETAILED DESCRIPTION

[0029] The examples of embodiment explained below are the preferred embodiments of the invention. In the examples of embodiment, the described components of the embodiment each represent individual features of the invention which must be considered independently of each other and which further develop the invention independently of each other and must thus also be regarded individually or in a combination other than the one shown as part of the invention. In addition, the forms of embodiment described can be supplemented by other features of the invention already described.

[0030] In the figures, identical reference numerals denote elements with the same functions.

[0031] FIG. 1 shows a motor vehicle 10 which may be a motor vehicle, in particular a passenger car or a truck. The motor vehicle 10 may have a control device 11 which may be designed, for example, as an infotainment system of the motor vehicle 10. A display device 12 and a processor device 13 of the operating device 11 are displayed. The display device 12 can be controlled by the processor device 13. The display device 12 can, for example, be a touch screen. At least one control element 14 can be shown on the display device 12, as it is further explained in connection with FIG. 2.

[0032] The operating device 11 can control at least one motor vehicle function 15 of the motor vehicle 10 depending on an operation or actuation of at least one control element 14. Such a motor vehicle function can include, for example, the radio or telephone or media playback in the motor vehicle 10. To control the respective motor vehicle function 15, the operating device 11 can generate the respective control data 16. The operating device 11 can observe or recognize the current driving situation. For this purpose, the operating device 11 can receive observation data 17, which describe, for example, a current temperature and/or a current destination. The observation data 17 can, for example, be generated in the operating device 11 itself (not symbolized in FIG. 1) and/or received from the Internet, for example from a data source (for example an online weather service and/or online traffic service) and/or be generated by at least one sensor device of the motor vehicle 10 and/or describe at least one state of at least one vehicle component. This results in a driving situation depending on the current values described in the observation data 17. Furthermore, usage data 18 can be determined in the operating device 11. The usage data 18 describe which motor vehicle function 15 is currently activated, i.e., has been selected by a user and activated by means of a control element 14. In addition or alternatively, the usage data 18 can indicate which function parameter has been set by a user in the respective at least one motor vehicle function 15, for example by means of a control element 14.

[0033] The operating device 11 can be used to predict which settings relating to vehicle functions 15 to be activated and/or function parameters to be adjusted a user will select in a respective additional driving situation. For this purpose, an assignment rule 20 is generated by means of the observation data 17 and the usage data 18 of at least one driving situation by means of a method of machine learning 19. For example, the assignment rule 20 may comprise a trained neural network. In addition, a decision rule 21 can, for example, be provided downstream of the neural network. By means of the assignment rule 20, a configuration data record 22, for example, which determines which control element 15 is to be offered to the user, can then be generated as a function of further observation data 17 which describe an additional driving situation for which the usage data 18 is not yet available, because the user will select this displayed control element 14 with a predetermined minimum probability which can be determined by the assignment rule.

[0034] FIG. 2 illustrates this using the example of a menu level 23 of a menu structure, in which six control elements 14 are shown in the example of FIG. 2 with the unadapted operating device 11, i.e., according to a factory setting. The control elements 14 can be implemented as icons or buttons or graphic symbols, for example. Depending on the observation data 17, the assignment rule 20 can specify that three of the control elements (crosshatched in FIG. 2) are deleted by an adaptation 24 because the respective motor vehicle function assigned to these control elements is operated with less than a predetermined minimum probability in the current driving situation as described by the current observation data 17. The adapted menu level 23 therefore only contains those control elements 14 which are operated or selected with a predetermined minimum probability in the current driving situation. This provides more space on the display device 12 for the remaining control elements. In comparison to the unadapted state, the control elements can therefore also be displayed in larger form. This makes it easier for a user to hit or touch the remaining control elements 14 with a finger, for example.

[0035] Thus, based on the fact that a person usually makes subconscious decisions according to a certain scheme, the user interface, which is displayed on the display device 12, is dynamically adapted to these conditions. For this purpose, a user/behavior analysis is performed in real time using artificial neural networks. For example, multi-layer perceptrons may be provided. A user behavior pattern can be recognized on the basis of their usage data and the additionally recorded observation data. The regularities or correlations or conditions discovered in this way can be used for the adaptation of the user interface and optionally determine the degree of adaptation by means of a decision rule 21 by only carrying out an adaptation 24 in a predetermined connection grid. A separate artificial neural network may be provided for each user interface which can be displayed on the display device 12 by means of the operating device 11. This means that the procedure of machine learning 19 can also distinguish between the individual, intended user interfaces by switching between a respective artificial neural network. Various observation data 17, such as sensor data, are included in the individual networks in order to implement pattern recognition. Examples of such observational data include, inter alia, information on the day, time, motion and/or geo-position of the motor vehicle.

[0036] This results in an individual optimization of the user interface according to user behavior, including various observation data using a procedure of machine learning 19.

[0037] The operating device 11 can therefore be personalized automatically and individually, which in turn can lead to an increase in operating speed, for example. As long searching through a control structure no longer forces a user to be careless in road traffic, this is also less tiring.

[0038] The observation data from the motor vehicle and the usage data can be provided to the processor device 13 in which these data (observation data 17, usage data 18) can be used in such a way by means of the procedure of machine learning 19, for example by an artificial neural network for training. Depending on the application, the neural networks can be adapted to achieve the best possible results.

[0039] FIG. 2 illustrates how the number of control elements 14 to be displayed can be reduced by the adaptation 24 of an operating level, for example a main menu. However, before each adaptation 24 is made, a user is preferably informed of the forthcoming adaptation 24 and the user is offered the option to decide or select whether to reject the adaptation 24. If the adaptation 24 is rejected, the operating level remains unchanged. This results in the advantage that, from the user's point of view, a control element 14 cannot simply disappear and they therefore have to search for it with even more effort.

[0040] The illustrated adaptation 24 can also be used for a submenu (for operating a radio and/or media playback).

[0041] Possible applications include adapting a main menu to reduce an initial or factory-provided unadapted display of N control elements (N, for example, eight). This can be implemented after a predetermined minimum useful service life if it is ensured that sufficient usage data 18 and observation data 17 have been collected. As a result, for example, a reduction to fewer than the N control elements can be achieved, for example to three control elements, as shown in FIG. 2. The less used control elements can be moved to a spare operating level.

[0042] Further possible applications are as follows: Telephone numbers and/or navigation destinations and/or radio stations can be sorted according to day and/or time. A motor vehicle setting, such as seat heating on/off, can be provided depending on the weather conditions and/or interior temperature. A radio station can be set according to day of the week and/or time of day.

[0043] Overall, the examples show how the invention can dynamically adapt a user interface to a driver's habits.