METHOD OF OPERATING A MOTOR VEHICLE

Abstract

A method of operating a motor vehicle includes detecting at least one motor vehicle driver response, producing a perception model based on the detected motor vehicle driver response, and analyzing the perception model to at avoid least one motor vehicle driver response by adjusting a parameter of the motor vehicle. Analysis of the perception model can also be carried out to predict a motor vehicle driver response, in particular during the generation of new control software for the vehicle.

Claims

1. A method of operating a motor vehicle comprising: detecting at least one driver response; producing a perception model based on the detected driver response; and analyzing the perception model to avoid at least one motor vehicle driver response by adjusting a parameter of the motor vehicle.

2. The method as claimed in claim 1 further comprising: detecting motor vehicle state parameters; and assigning the motor vehicle state parameters to the driver response.

3. The method as claimed in claim 2 further comprising: detecting motor vehicle sensor values; and assigning the motor vehicle sensor values to the driver response.

4. The method as claimed in claim 3 further comprising: classifying the driver response as a positive and/or negative response.

5. The method as claimed in claim 1, wherein producing a perception model takes into account at least one motor vehicle driver input.

6. The method as claimed in claim 1, wherein the perception model is configured to predict a motor vehicle driver response.

7. A system for predicting a vehicle driver response comprising: one or more hardware components implementing software components including a classifying module configured to classify data, from sensors, indicative of a driver response, driving state, and vehicle response observation; an input module configured to transmit direct input of subjective driver impressions to a server having a database of the data; an optimizing module configured to produce a driver perception model from the data and impressions; and a design module configured to access the data on the database such that new driving situations are determined by a team based on the data and impressions.

8. The system as claimed in claim 7, wherein the classifying module is further configured to classify the data as a positive or negative response, wherein measurement values are assigned to negative responses.

9. The system as claimed in claim 7, wherein data indicative of the driver response includes pupil size, vocal pitch, heart rate, breathing rate, body movement, eye movement and skin resistance of a driver.

10. The system as claimed in claim 7, wherein data indicative of the driving state includes vehicle state parameters and vehicle sensor values.

11. The system as claimed in claim 7, wherein data indicative of the vehicle response observation includes analysis of vehicle behavior to detect patterns by cluster formation.

12. The system as claimed in claim 7 further comprising an interface connected to the input module, wherein a driver provides the direct input to the input module via the interface.

13. A vehicle comprising: a hardware controller programmed to: produce a driver perception model from data indicative of a driver response, driving state, and vehicle response observation and direct input of subjective driver impressions; and access the data, stored on a database, such that new driving situations are determined by a team based on the data.

14. The vehicle as claimed in claim 13 wherein the controller is further programmed to classify the data.

15. The vehicle as claimed in claim 14, wherein the controller is further programmed to classify the data as a positive or negative response, wherein measurement values are assigned to negative responses.

16. The vehicle as claimed in claim 13 wherein the controller is further programmed to transmit direct input of the data to a server of the database.

17. The vehicle as claimed in claim 16, further comprising an interface connected to the controller, wherein a driver provides the direct input to the controller via the interface.

18. The vehicle as claimed in claim 13 further comprising sensors configured to transmit data indicative of the driving state based on vehicle state parameters and sensor values to the controller.

19. The vehicle as claimed in claim 13, wherein data indicative of the vehicle response observation includes analysis of vehicle behavior to detect patterns by cluster formation.

20. The vehicle as claimed in claim 13 further comprising sensors configured to transmit data indicative of the driver response based on pupil size, vocal pitch, heart rate, breathing rate, body movement, eye movement and skin resistance of a driver to the controller.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The FIGURE shows a schematic representation of a system for producing a perception model.

DETAILED DESCRIPTION

[0020] As required, detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The FIGURES are not necessarily to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present disclosure.

[0021] A motor vehicle 2 is represented with control software 12 that controls various functions of the motor vehicle 2, such as, for example, the response of an engine, operation of a gas pedal and/or brakes, operation of a brake pedal, a steering behavior or a gear change in the case of an automatic gearbox. The control software 12 comprises variable parameters for this purpose.

[0022] Furthermore, in the present exemplary embodiment, the motor vehicle 2 comprises a driver response observation module 3. During the operation of the motor vehicle 2, using suitable sensors, the driver response observation module 3 detects pupil size, pitch of the voice, heart rate, breathing rate, body movement, eye movement and/or skin resistance of a driver of the motor vehicle 1, and combines the variables into driver response data.

[0023] Furthermore, in the present exemplary embodiment, the motor vehicle 2 comprises a driving state observation module 4. The driving state observation module 4 detects motor vehicle state parameters, such as speed of the motor vehicle, revolution rate of the engine, or acceleration or braking and/or motor vehicle sensor values, such as, for example, a motor vehicle position, a traffic situation with other road users or traffic signals, and combines said variables into driving state data.

[0024] Moreover, in the present exemplary embodiment the motor vehicle 2 comprises a motor vehicle response observation module 5. The motor vehicle response observation module 5 detects the motor vehicle behavior, and analyzes the behavior in order to detect patterns by cluster formation, such as, for example, acceleration patterns, impulse patterns, motor vehicle speed patterns, a noise level and/or a vibration level, and combines the variables into motor vehicle response data.

[0025] A classifying module 6 is associated with the motor vehicle 2. The classifying module 6 is designed to read the driver response data detected by the driver response observation module 3, and to classify the driver response data as positive or negative responses of the driver of the motor vehicle 1. For example, an increase in measurement values of pupil size, pitch of the voice, heart rate, breathing rate, body movement, eye movement and/or skin resistance are viewed as stress symptoms of the driver of the motor vehicle 1, and are, therefore, classified as negative responses. On the other hand, other responses can be classified as positive responses.

[0026] Furthermore, in the present exemplary embodiment the classifying module 6 is designed to read in the driving state data detected by the driving state observation module 4 and the motor vehicle response data detected by the motor vehicle response observation module 5, and assign respective classified responses in order to be able to assign measurement values to negative responses, for example.

[0027] Furthermore, in the present exemplary embodiment an input module 7 is associated with the motor vehicle 2. The input module 7 enables the direct input of subjective impressions of the driver of the motor vehicle 1, and the transmission of the subjective impressions to a central server, for example, by means of wireless data transmission. The input module 7 can for example be designed to prompt the motor vehicle driver 1 after the end of each journey to answer questions. The questions can be answered in a natural language and the input module 7 comprises speech recognition. Alternatively, the responses can be entered directly into windows of a menu, for example as numerical inputs in the form of 3 of 5 points, for example. With the agreement of the driver of the motor vehicle 1, the responses are transmitted to the server.

[0028] A database 8 with which the incoming data as well as the classified responses with the assigned data from the classifying module 6 are collected runs on the server.

[0029] Besides the input module 7, the motor vehicle driver can also make entries into the database 8 via an interface 9 to the database 8. The inputs can be independent of a menu and can thus be freely formulated, and can further be made at any time and from any location, i.e. not only immediately after a journey.

[0030] A software parameter optimizing module 10 has access to the data of the database 8. The software parameter optimizing module 10 analyzes the data, for example for negative responses, produces a perception model of motor vehicle drivers 1, for example based on neural networks or fuzzy logic, and thus enables a development team 13 to investigate the consequences of changes of parameters. Furthermore, it can be investigated in an automatic manner how changes of parameters affect the frequency of negative responses, and a new set of parameters is determined using a quality criterion that weights the various negative responses differently.

[0031] Furthermore, a design module 11 has access to the data of the database 8. For example, new driving situations for which an adjustment of software that goes beyond the adjustment of parameters is necessary can be determined by the development team 13 or by algorithms, for example for the classification of data or for example in order to estimate how drivers would react to software changes.

[0032] The driver response observation module 3, the driving state observation module 4, the motor vehicle response observation module 5, the classifying module 6, the input module 7, the database 8, the interface 9, the software parameter optimizing module 10, the software design module 11 and the control software 12 can comprise hardware components and/or software components for this purpose.

[0033] During operation, the driver response observation module 3 detects pupil size, pitch of the voice, heart rate, breathing rate, body movement, eye movement and/or skin resistance of a driver of the motor vehicle 1, and combines the variables into driver response data.

[0034] Furthermore, the driving state observation module 4 detects motor vehicle state parameters, such as speed of the motor vehicle, revolution rate of an engine, or acceleration or braking and/or motor vehicle sensor values, such as for example a motor vehicle position, a traffic situation with other road users or traffic signals, and combines the variables into driving state data.

[0035] Furthermore, the motor vehicle response observation module 5 detects the motor vehicle behavior, and analyzes the behavior in order to detect patterns by cluster formation, such as, for example, acceleration patterns, impulse patterns, motor vehicle speed patterns, a noise level and/or a vibration level, and combines the variables into motor vehicle response data.

[0036] The classifying module 6 reads in the detected driver response data, and classifies the driver response data as positive or negative responses of the driver of the motor vehicle 1.

[0037] Furthermore, the classifying module 6 reads in the driving state data and the motor vehicle response data, and assigns the driving state and motor vehicle data to the respective classified responses.

[0038] At the end of a journey, the motor vehicle driver 1 can make inputs with the input module 7 that are transmitted to the server with the agreement of the driver of the motor vehicle 1. Alternatively, the motor vehicle driver can also make inputs directly via the interface 9 to the database inputs.

[0039] The software parameter optimizing module 10 analyzes the data for negative responses, and produces a perception model of motor vehicle drivers 1, whereas with the software design module 11 new driving situations for which an adjustment of software that goes beyond the adjustment of parameters is necessary can be determined.

[0040] The occurrence of corresponding driving situations can thus be counteracted by a suitable adjustment of motor vehicle parameters.

[0041] While exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms of the disclosure. Rather, the words used in the specification are words of description rather than limitation, and it is understood that various changes may be made without departing from the spirit and scope of the disclosure. Additionally, the features of various implementing embodiments may be combined to form further embodiments of the disclosure.