Method for providing plurality of driving modes for at least one motor vehicle, corresponding computer program product, corresponding non-transitory computer-readable storage medium and corresponding central electronic computing device

Abstract

The invention relates to a method for providing a plurality of driving modes (12) for at least one motor vehicle (14) by a central electronic computing device (10), the method comprising the steps of: receiving, by the electronic computing device (10), a plurality of driving behaviors (16) of a plurality of motor vehicles (18); (S1) extracting, by the electronic computing device (10), similarities in the plurality of driving behaviors (16); (S2) clustering, by the electronic computing device (10), the plurality of driving behaviors (16) according to similarities; (S3) generating, by the electronic computing device (10), a plurality of different driving modes (12) from the clustering; (S4) and providing the plurality of driving modes (12) for the at least one motor vehicle (14). Furthermore, the invention relates to a computer program product, a non-transitory computer-readable storage medium and a central electronic computing device (10).

Description

Technical Field

[0001] The present invention relates to the automotive field. More specifically, the present invention relates to a method for providing multiple driving modes for at least one motor vehicle by a central electronic computing device. Furthermore, the present invention relates to a computer program product, a corresponding non-transitory computer-readable storage medium, and a central electronic computing device. Background Art

[0002] Driving style is highly personal and subjective. The same ADAS (Advanced Driver Assistance System) may work well for one person but feel overly cautious or passive for another. Therefore, it is desirable to have a preconfigured set of different / distinct driving styles that match and suit a large portion of the customer base. Furthermore, it is desirable to identify relevant parameters that are key factors in different driving styles. Summary of the Invention

[0003] The object of the present invention is to provide a method, a computer program product, a non-transitory computer-readable storage medium and a central electronic computing device, by which a plurality of driving modes are provided to a user of a motor vehicle.

[0004] This object is solved by a method, a computer program product, a non-transitory computer-readable storage medium and by a central electronic computing device according to the independent claims. Advantageous embodiments are presented in the dependent claims.

[0005] One aspect of the present invention relates to a method for providing multiple driving profiles for at least one motor vehicle by a central electronic computing device. The electronic computing device receives multiple driving behaviors of multiple motor vehicles. The electronic computing device extracts similarities among the multiple driving behaviors. The electronic computing device clusters the multiple driving behaviors based on the similarities. The electronic computing device generates multiple different driving profiles based on the clusters, and provides the multiple driving profiles to the at least one motor vehicle.

[0006] Therefore, the present invention is configured to find relevant parameters that characterize driving styles based on fleet behavior. In addition, the detected driving styles of customer fleets are aggregated in clusters to generate a set of different parameter configurations that best reflect different driving styles.

[0007] Thus, this invention describes a system for identifying parameters that correlate with different driving styles based on monitoring human drivers' driving behavior. These parameters can be manually defined parameters that influence driving style, such as maximum acceleration, the duration of a lane change, the distance to the vehicle ahead, or speed related to lane curvature. Furthermore, this data-driven approach can construct latent features that provide information about driving style but may not have direct semantic meaning.

[0008] According to an embodiment, a plurality of different driving modes are presented for selection on a display device of a motor vehicle.

[0009] In another embodiment, the different driving modes are presented before the motor vehicle begins moving.

[0010] In another embodiment, the plurality of driving behaviors are clustered by parameters of maximum acceleration, duration taken for lane change, distance to the preceding vehicle, or speed related to lane curvature.

[0011] In another embodiment, at least one setting of a dynamic driving device of the motor vehicle is adjusted as a function of the different driving modes selected.

[0012] In another embodiment for extracting similarities, a data-driven approach is used.

[0013] In particular, the method is a computer-implemented method. Therefore, another aspect of the present invention relates to a computer program product comprising program code means for executing the method according to the aforementioned aspects. In particular, the computer program product can also be regarded as a computer program.

[0014] Yet another aspect of the present invention relates to a non-transitory computer-readable storage medium, which comprises at least the computer program product according to the aforementioned aspect.

[0015] Yet another aspect of the present invention relates to a central electronic computing device for providing a plurality of driving modes for at least one motor vehicle, wherein the central electronic computing device is configured to execute the method according to the aforementioned aspect. In particular, the method is executed by the central electronic computing device.

[0016] The central electronic computing device may comprise a processor, an electrical circuit (in particular an integrated circuit) and further electronic means for executing the method according to the aforementioned aspects.

[0017] Further advantages, features and details of the invention are derived from the following description of preferred embodiments and the accompanying drawings. The features and feature combinations mentioned above in the description and the features and feature combinations mentioned in the following description of the drawings and / or shown individually in the drawings can be used not only in the respectively indicated combination but also in any other combination or alone without departing from the scope of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The novel features and characteristics of the present disclosure are set forth in the appended claims. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the principles disclosed. Like numerals are used throughout the drawings to represent like features and components. Some embodiments of systems and / or methods according to embodiments of the present subject matter are now described below, by way of example only, with reference to the accompanying drawings.

[0019] In the attached figure:

[0020] Figure 1 is a schematic block diagram of an embodiment of a central electronic computing device according to the present invention.

[0021] In the drawings, identical elements or elements having the same function are denoted by the same reference numerals. DETAILED DESCRIPTION

[0022] In this document, the word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment or implementation of the present subject matter described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0023] Although the present disclosure is susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and will be described in detail below. However, it should be understood that the present disclosure is not intended to be limited to the particular forms disclosed, but rather, the present disclosure is intended to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure.

[0024] The term "comprise," or any other variation thereof, is intended to cover a non-exclusive inclusion, such that an arrangement, apparatus, or method that comprises a list of components or steps includes not only those components or steps, but may also include other components or steps not expressly listed or inherent to such arrangement, apparatus, or method. In other words, the presence of one or more elements in a system or apparatus preceded by the word "comprise" does not, without more limitations, preclude the presence of other or additional elements in the system or method.

[0025] In the following detailed description of the embodiments of the present disclosure, reference is made to the accompanying drawings which form a part hereof and in which are shown by way of illustration specific embodiments in which the present disclosure may be practiced. This embodiment is described in sufficient detail to enable one skilled in the art to practice the present disclosure, and it is understood that other embodiments may be utilized and changes may be made without departing from the scope of the present disclosure. Therefore, the following description should not be considered limiting.

[0026] Figure 1 A schematic block diagram of an embodiment according to the present invention is shown. In particular, Figure 1 A block diagram is shown according to an embodiment of a central electronic computing device 10. The central electronic computing device 10 may include a processor, circuits (e.g., integrated circuits), and other electronic devices for executing the methods according to the present invention. In particular, the central electronic computing device may be configured as a backend server.

[0027] According to an embodiment of the present invention, a method is provided for providing a plurality of driving modes 12 for at least one motor vehicle 14 by a central electronic computing device 10. In a first step S1, the electronic computing device 10 receives a plurality of driving behaviors 16 of a plurality of motor vehicles 18. In a second step S2, the electronic computing device 10 extracts similarities among the plurality of driving behaviors 16. In a third step S3, the electronic computing device 10 clusters the plurality of driving behaviors 16 based on the similarities. In a fourth step S4, the electronic computing device 10 generates a plurality of different driving modes 12 based on the clustering, and provides the plurality of driving modes 12 to the at least one motor vehicle 14. In particular, the plurality of different driving modes 12 are presented for selection on a display device 20 of the motor vehicle 14. The different driving modes 12 may be presented before the motor vehicle 14 begins to move.

[0028] Furthermore, at least one setting of a dynamic driving device 22 of the motor vehicle 14 is adjusted depending on the selected different driving mode 12. For example, a braking device or a steering device or an acceleration device as dynamic driving device 22 may be adjusted.

[0029] In particular, Figure 1 A system is described for identifying parameters that correlate with different driving styles based on monitoring human drivers' driving behavior. These parameters can be manually defined parameters that influence driving style, such as maximum acceleration, the duration of a lane change, the distance to the vehicle ahead, or speed related to lane curvature. Furthermore, this data-driven approach can construct latent features that provide information about driving style but may not have direct semantic meaning.

[0030] Driving behaviors 16 from multiple vehicles 18 are aggregated in the backend, and clustering techniques are used to identify parameters that correlate with different driving styles. Furthermore, the data-driven approach allows for clustering different behaviors from the fleet into a smaller set of distinct styles. Clustering driving behaviors across the entire customer fleet allows for deriving overall driving patterns as a cross-section / representative sample of the customer base / customer population, and providing these as options for new drivers, pre-configured driving modes for vehicles 14, or explicit options when starting a drive or moving a vehicle.

[0031] Finally, the dynamic driving device (which may be, for example, an ADAS system) has an adaptive driving style that changes the identified parameters according to the selected preset.

[0032] Thus, a pre-adjustment of the ADAS system is provided that supports different driving styles to meet the corresponding subjective customer perception. Thus, monitoring of the driving behavior 16 of human drivers in a customer fleet is provided. Parameters that are correlated factors of different driving styles are extracted and their impact on a data-driven approach is quantified. Clustering of the driving behavior 16 of the entire customer fleet is provided to derive overall driving patterns as a cross-section / representative sample of the customer base / customer group. Based on the clustering results, a definition of a small set of different / distinct driving styles with corresponding parameter configurations is provided. At the start of driving or when the vehicle starts moving, a map or new driver profile with clear options or driving mode selection is suggested to the driver of the motor vehicle 14. The adaptive ADAS system can be configured with a set of selected parameters that are a derived driving style found in the fleet behavior.

[0033] Reference numerals

[0034]

Claims

1. A method for providing a plurality of driving modes (12) for at least one motor vehicle (14) by a central electronic computing device (10), the method comprising the following steps: - receiving a plurality of driving behaviors (16) of a plurality of motor vehicles (18) by the electronic computing device (10); (S1) - extracting similarities among the plurality of driving behaviors (16) by the electronic computing device (10); （S2） - clustering the plurality of driving behaviors (16) by the electronic computing device (10) according to the similarity; (S3) - generating a plurality of different driving modes (12) according to the clustering by the electronic computing device (10); (S4) and - providing said plurality of driving modes (12) for said at least one motor vehicle (14).

2. The method according to claim 1, It is characterized by A plurality of different driving modes (12) are presented for selection on a display device (20) of the motor vehicle (14).

3. The method according to claim 2, It is characterized by The different driving modes (12) are presented before the motor vehicle (14) begins moving.

4. The method according to any one of claims 1 to 3, It is characterized by The plurality of driving behaviors (16) are clustered by parameters of maximum acceleration, duration of lane change, distance to a preceding vehicle, or speed related to lane curvature.

5. The method according to any one of claims 1 to 4, It is characterized by At least one setting of a dynamic driving device (22) of the motor vehicle (14) is adjusted according to the selected different driving modes (12).

6. The method according to any one of claims 1 to 5, It is characterized by To extract the similarities, a data-driven approach is used.

7. A computer program product comprising program code means for executing the method according to any one of claims 1 to 6. 8 . A non-transitory computer-readable storage medium comprising at least the computer program product according to claim 7 .

9. A central electronic computing device (10) for providing a plurality of driving modes (12) for at least one motor vehicle (14), wherein: The central electronic computing device (10) is configured to execute the method according to any one of claims 1 to 6.

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