DE102022112062A1 - Method, system, control module and computer program product for classifying driving styles in a motor vehicle - Google Patents

Abstract

The invention relates to a method for classifying the driving styles of a motor vehicle when driving along a route, the motor vehicle comprising at least one control module with at least one processor and a memory module, with the method steps: - Determining measurement data using the sensors of parameters of the motor vehicle in a temporal manner Sequence when driving on the route or route segment; - Sending the measurement data to the control module and / or to a cloud computing infrastructure and / or a user interface; - Processing the measurement data from the control module and / or the cloud computing infrastructure and / or the user interface for determining a first characteristic value for identifying the driving style in the first time window and / or the first route section; - storing the determined characteristic value for the first time window and / or the first route section; - determining a further characteristic value for identifying the driving style in each case the respective subsequent time window and/or the respective subsequent route section; - storing the respective determined characteristic value for the respective time window and/or the respective route section; - creating a histogram and/or a key core score value for the determined characteristic values.

Description

The present invention relates to a method, a system, a control module and a computer program product for classifying driving styles in a motor vehicle.

A driver of a motor vehicle often selects his driving style based on a traffic situation or his personal preferences. Depending on the desired driving style, the driver expects the motor vehicle to behave in a certain way. A comfortable driving style is therefore associated with a quiet and balanced driving behavior of the vehicle, which is reflected, for example, in a low speed level. On the other hand, a sporty driving style means fast, active driving behavior with higher acceleration values.

It is also possible to calculate a quantifiable sportiness of the driving style, particularly based on the longitudinal and lateral acceleration as well as the accelerator pedal activities, for example with values on a scale from 0 to 1. In the case of an internal combustion engine, other parameters such as engine speed, engine torque or time can also be used The course of the switching processes in the transmission can be used. However, a value is only assigned to a current point in time during a journey, while the driving style over a longer route has not yet been recorded and therefore not classified. The boundary between a comfortable driving style and a sporty driving style is also not clearly defined. In addition, it may be of interest for a driver to know before starting a journey what level of sportiness can actually be achieved on a selected route due to local conditions and legal requirements such as speed limits.

The DE 10 2013 209792 B4 describes a method for determining driver type, whereby a driver's driving activity is automatically determined from vehicle parameters such as lateral acceleration and longitudinal acceleration and a driver type is automatically determined depending on the driving activity and the respective route. A distinction is made between a sporty and an economical driving style.

The EP 3335951 B1 describes the calculation of a driving style sport index based on an accelerator pedal gradient and lateral or longitudinal acceleration.

The DE 10 2013 209787 A1 describes a method for determining driver type, wherein a driving activity of a driver is automatically determined from vehicle parameters of a vehicle, in particular from the speed and / or the longitudinal acceleration of the vehicle, and a driver type is automatically determined depending on the driving activity, and wherein the driver type continues to depend on predictive route parameters a route in front of the vehicle is determined.

It is therefore an object of the present invention to provide a method, a system, a control module and a computer program product for classifying driving styles in a motor vehicle, which enables improved analysis and evaluation over a longer period of time.

This object is achieved according to the invention with regard to a method by the features of patent claim 1, with regard to a system by the features of patent claim 13, with regard to a control module by the features of claim 14 and with regard to a computer program product by the features of claim 15. The further claims relate to preferred embodiments of the invention.

The invention makes it possible to determine and evaluate the driving styles of a motor vehicle over a longer period of time, so that, for example, the condition of a motor vehicle after prolonged use can be characterized not only by the mileage but also by the driving styles used.

• - Ermitteln von Messdaten mittels der Sensoren von Parametern des Kraftfahrzeugs in einer zeitlichen Sequenz beim Befahren der Fahrstrecke oder des Fahrstreckensegments;
• - Senden der Messdaten an das Steuerungsmodul und/oder an eine Cloud-Computing-Infrastruktur und/oder eine Benutzerschnittstelle;
• - Bearbeiten der Messdaten von dem Steuerungsmodul und/oder der Cloud-Computing-Infrastruktur und/oder der Benutzerschnittstelle zur Ermittlung eines ersten Kennwertes zur Kennzeichnung der Fahrweise in dem ersten Zeitfenster Δt₁ und/oder dem ersten Fahrstreckenabschnitt Δx₁;
• - Speichern des ermittelten ersten Kennwertes für das erste Zeitfenster Δt₁ und/oder den ersten Fahrstreckenabschnitt Δx₁;
• - Ermitteln jeweils eines weiteren Kennwertes zur Kennzeichnung der Fahrweise in dem jeweiligen nachfolgenden Zeitfenster Δt₂, ..., Δt_n und/oder dem jeweiligen nachfolgenden Fahrstreckenabschnitt Δx₂, ..., Δx_n beim Befahren der Fahrstrecke oder des Fahrstreckensegments;
• - Speichern des jeweils ermittelten Kennwertes für das jeweilige Zeitfenster Δt₂, ..., Δt_n und/oder den jeweiligen Fahrstreckenabschnitt Δx₂, ..., Δx_n der Fahrstrecke oder des Fahrstreckensegments;
• - Erstellen eines Diagramms wie eines Histogramms und/oder eines Key Core Score Wertes wie eines Mittelwertes für die ermittelten Kennwerte während des Befahrens der Fahrstrecke und/oder des Fahrstreckensegments.

According to a first aspect, the invention relates to a method for classifying the driving styles in a motor vehicle when driving on a route or a route segment, wherein the motor vehicle has a drive system, a drive train, wheels, brake systems, sensors, a data communication system and at least one control module with at least one processor and a memory module, and wherein the route or the route segment is divided into a sequence of route sections Δx ₁ , Δx ₂ , ..., Δx _n and a sequence of time windows Δt ₁ , Δt ₂ , ..., Δt _n , where a Time window Δt _i is assigned to a route section Δx _i or is determined independently of a route section Δx _i . The procedure includes the following procedural steps:

• - Determining measurement data using the sensors of parameters of the motor vehicle in a time sequence when driving on the route or the route segment;
• - Sending the measurement data to the control module and/or to a cloud computing infrastructure and/or a user interface;
• - Processing the measurement data from the control module and/or the cloud computing infrastructure and/or the user interface to determine a first characteristic value for identifying the driving style in the first time window Δt ₁ and/or the first route section Δx ₁ ;
• - storing the determined first characteristic value for the first time window Δt ₁ and/or the first route section Δx ₁ ;
• - Determining a further characteristic value for identifying the driving style in the respective subsequent time window Δt ₂ , ..., Δt _n and / or the respective subsequent route section Δx ₂ , ..., Δx _n when driving on the route or the route segment;
• - storing the respective determined characteristic value for the respective time window Δt ₂ , ..., Δt _n and/or the respective route section Δx ₂ , ..., Δx _n of the route or the route segment;
• - Creating a diagram such as a histogram and/or a Key Core Score value such as an average value for the characteristic values determined while driving the route and/or the route segment.

In a further development, it is provided that low characteristic values are assigned a comfortable driving style, medium characteristic values are assigned a balanced driving style, and high characteristic values are assigned a sporty driving style.

In particular, the characteristic values are on a scale from 0 to 1.

In an advantageous embodiment it is provided that the parameters of the motor vehicle include a speed and/or a lateral acceleration and/or a longitudinal acceleration and/or an accelerator pedal activity and/or a brake pedal activity and/or a torque of the drive system and/or a tire slip.

A GPS connection is advantageously provided and a geographical location is assigned to the respective time window Δt ₁ , Δt ₂ , ..., Δt _n and/or the respective route section Δx ₁ , Δx ₂ , ..., Δx _n .

In a further embodiment it is provided that the route and/or the route segment is entered on a navigation device and the length of a time window Δt ₁ , Δt ₂ , ..., Δt _n and/or a route section Δx ₁ , Δx ₂ , .. ., Δx _n is determined depending on a topography and / or a traffic situation of the route or the route segment.

This means that the length of a time window or a route section is adapted to geographical or traffic conditions. By varying the length of a time window, computing capacity can be used more effectively, since it is not necessary to calculate a characteristic value if it is foreseeable that, for example, the driving style will remain essentially constant on a longer stretch of the motorway.

In particular, the data sent to the cloud computing infrastructure is cryptographically encrypted using a software application. This ensures that the data cannot be manipulated by third parties.

In a further development it is provided that the control module is equipped with at least one radio module for communication with the cloud computing infrastructure and/or the user interface.

In a further embodiment it is provided that a diagram such as a histogram and/or a key core score value such as an average value for the characteristic values determined on a route is created for a large number of routes and an overall diagram and/or is created from the large number of diagrams An overall Key Core Score value is determined from the large number of Key Core Score values.

In particular, it is provided that the control module and/or the cloud computing infrastructure and/or the user interface uses artificial intelligence algorithms such as reinforcement learning and/or neural networks to determine the characteristic values for identifying the driving styles and/or for creating a diagram/ use.

Advantageously, the measurement data determined by the sensors are transmitted to the control module using the data communication system.

In an advantageous embodiment it is provided that the diagram and/or the characteristic values as well as further evaluation results are displayed on the user interface using a software application.

According to a second aspect, the invention relates to a system for classifying driving in a motor vehicle when driving along a route and/or a route segment. The system includes a motor vehicle with a drive system, a drive train, wheels, braking systems, sensors, a data communication system and at least one control module with at least one processor and a memory module. The system is designed to carry out the method according to the first aspect.

According to a third aspect, the invention relates to a control module of a motor vehicle with at least one processor and a memory module. The system is designed to carry out the method according to the first aspect.

According to a fourth aspect, the invention provides a computer program product comprising executable program code configured to execute the method according to the first aspect.

Further features, aspects and advantages of the invention or its embodiments are explained in the detailed description in conjunction with the following figures:

• 1 eine schematische Darstellung des erfindungsgemäßen Systems;
• 2 eine graphische Darstellung eines beispielhaften Histogramms;
• 3 eine graphische Darstellung der Zuordnung von Fahrweisen zu Kennwerten;
• 4 ein Flussdiagramm zur Erläuterung der einzelnen Verfahrensschritte des erfindungsgemäßen Verfahrens;
• 5 eine schematische Darstellung eines Computerprogrammprodukts.

This shows:

• 1 a schematic representation of the system according to the invention;
• 2 a graphical representation of an example histogram;
• 3 a graphical representation of the assignment of driving styles to characteristic values;
• 4 a flowchart to explain the individual process steps of the method according to the invention;
• 5 a schematic representation of a computer program product.

In the following description, specific details are set forth for purposes of explanation and not limitation in order to provide a thorough understanding of the present invention. It will be apparent to those skilled in the art that the present invention may be embodied in other implementations that depart from these specific details.

In 1 a system 100 for classifying the driving style of a motor vehicle 20 is shown. The motor vehicle 20 includes an interior and is driven by a drive system 21. The drive system 21 may include a conventional internal combustion engine, or an internal combustion engine and at least one electric motor for a hybrid drive, or at least one electric motor or at least one fuel cell or another drive source. Furthermore, a data communication system 30 is provided, which communicates with the drive system 21 and various motor vehicle modules such as wheels 25 and braking systems 27 as well as sensors 29. The data communication system 30 can be designed as a CAN bus (Control Area Network) or Flexray data channel. Furthermore, a vehicle battery is provided, which can include different battery types and supplies the various modules and components of the motor vehicle 20 with electrical energy. In the case of a hybrid vehicle or an electric vehicle, the vehicle battery also includes a traction battery for the electric motor. Furthermore, a fuel cell can be provided.

The sensors 29 determine measurement data 50 of parameters of the motor vehicle 20. The parameters are in particular a speed, a lateral acceleration, a longitudinal acceleration, an accelerator pedal activity, a brake pedal activity, a torque of the drive system 21 and / or tire slip. The sensors 29 are therefore designed in particular as accelerometers, speedometers, torque counters, tachometers, distance meters and/or capacitive and inductive sensors.

The drive system 21, the data communication system 30, the sensors 29 and the motor vehicle modules such as the wheels 25 and the braking systems 27 are connected to a control module 40. The control module 40 comprises at least one processor 41 and at least one memory module 43. Furthermore, the control module 40 can advantageously be equipped with a mobile radio module 45 for communication with a cloud computing infrastructure 70. In addition, the control module 40 can communicate with a user interface 49 by means of the mobile radio module 45. The user interface 49 is designed in particular as a mobile radio device, but can also be permanently installed in the motor vehicle 20. Furthermore, a communication connection between the cloud computing infrastructure 70 and the user interface 49 may be provided.

Furthermore, a GPS connection (Global Positioning System) 80 is advantageously provided in order to determine the geographical location of the motor vehicle 20. However, position data displayed by the user interface 49 designed as a mobile radio device can also be used.

In connection with the invention, a “processor” can, for example, be a Machine or an electronic circuit can be understood. A processor can in particular be a main processor (Central Processing Unit, CPU), a microprocessor or a microcontroller, for example an application-specific integrated circuit or a digital signal processor, possibly in combination with a memory unit for storing program instructions, etc . A processor can also be understood as a virtualized processor, a virtual machine or a soft CPU. For example, it can also be a programmable processor that is equipped with configuration steps for carrying out the said method according to the invention or is configured with configuration steps in such a way that the programmable processor has the features of the method, the component, the modules, or other aspects and/or other aspects according to the invention. or partial aspects of the invention are realized. In addition, an AI hardware acceleration such as the Coral Dev Board can advantageously be used for the processor 41, in particular to enable processing in real time. This is a microcomputer with a tensor processing unit (TPU).

In connection with the invention, a “memory unit” or a “memory module” and the like can, for example, mean a volatile memory in the form of a random access memory (RAM) or a permanent memory such as a hard drive or a data carrier or, for example. B. a replaceable memory module can be understood. The storage module can also be a cloud-based storage solution. Furthermore, it is conceivable to use the memory module of a mobile radio device.

In connection with the invention, a “module” can be understood to mean, for example, a processor and/or a memory unit for storing program instructions. For example, the processor is specifically set up to execute the program instructions in such a way that the processor and/or the control unit executes functions in order to implement or realize the method according to the invention or a step of the method according to the invention.

In particular, the mobile radio module 45 can have the 5G standard. 5G is the fifth generation mobile communications standard and, compared to the 4G mobile communications standard, is characterized by higher data rates of up to 10 Gbit/sec, the use of higher frequency ranges such as 2100, 2600 or 3600 megahertz, increased frequency capacity and thus increased data throughput and real-time data transmission because up to a million devices per square kilometer can be addressed simultaneously. The latency times range from a few milliseconds to less than 1 ms, so that real-time transmission of data and calculation results is possible. The measurement data 50 determined by the sensors 29 can be sent by the control module 40 to the cloud computing infrastructure 70 in real time. The cloud computing infrastructure 70 can be used both for processing the measurement data 50 and for storing measurement data 50 and calculation results. Cloud-based solutions offer the advantage of high and therefore fast computing power and large storage capacities. This is of great advantage in the context of long-term storage of determined measurement data 50 and the calculation results created therefrom, since the storage capacities of the memory module 43 of the motor vehicle 20 are often not sufficient for storing large amounts of data.

Furthermore, the mobile radio module 45 can have a near field communication connection such as ^Bluetooth® , Ethernet, NFC (near field communication) or Wi-Fi® and can therefore communicate with the user interface 49, for example.

A driver of the motor vehicle 20 often selects his driving style based on a traffic situation or his personal preferences. A comfortable driving style is associated with a smooth and balanced driving behavior of the vehicle, which is reflected, for example, in a low speed level. On the other hand, a sporty driving style is associated with fast and active driving behavior with higher acceleration values depending on the route profile.

Based on the measurement data 50 of vehicle parameters such as an accelerator pedal gradient, lateral and longitudinal acceleration and tire slip determined by the sensors 29, a characteristic value for the driving style of the motor vehicle 20, such as a sportiness index value, can be calculated in order to thereby quantify the driving style. In motor vehicles 20 with a manual transmission, a characteristic value for the current driving style can also be derived from the switching behavior. An assessment of the sportiness of the route can also be included in the sportiness index. A stretch of motorway can be evaluated with a different factor than a winding country road. In addition, the volume of traffic, a specific traffic situation, the weather situation, specified speed limits, etc. can be taken into account. In particular, it is provided that the value range of the characteristic values is on a scale between 0 and 1. However, a different scaling can also be carried out.

Like in the 2 shown, the characteristic values are assigned to three driving styles on a scale from 0 to 1, which are described as comfortable, balanced and sporty. Comfortable is defined as a driving style whose characteristic values are in the value range between 0 and 0.2, balanced is defined as a driving style whose characteristic values are between 0.2 and 0.4, and the characteristic values between 0.4 and 1 characterize one sporty driving style. For example, these defined limit values of the characteristic values can be based on a 99.985% confidence interval during test drives carried out with test vehicles. However, other limit values can also be provided for defining a driving style.

The calculation of the characteristic values can take place in the control module 40 and/or the cloud computing infrastructure 70. Artificial intelligence algorithms can be used to analyze the measured values and calculate the characteristic values. In particular, reinforcement learning algorithms and/or neural networks can be used.

A neural network consists of neurons arranged in several layers and connected to each other in different ways. A neuron is able to receive information from outside or from another neuron at its input, evaluate the information in a certain way and forward it in a modified form to another neuron at the neuron output or output it as the end result. Hidden neurons are located between the input neurons and output neurons. Depending on the network type, there may be multiple layers of hidden neurons. They ensure the forwarding and processing of the information. Output neurons ultimately deliver a result and output it to the outside world. The arrangement and connection of the neurons creates different types of neural networks such as feedforward networks, recurrent networks (RNN) or convolutional neural networks (CNN). The networks can be trained using unsupervised or supervised learning

A route or a route segment is divided into a sequence of route sections Δx ₁ , Δx ₂ , ..., Δx _n and a sequence of time windows Δt ₁ , Δt ₂ , ..., Δt _n , where a time window Δt _{i corresponds to} a route section Δx _i can be assigned, but can also be determined independently of a route section Δx _i . According to the invention, while driving a defined route or a route segment, a characteristic value is provided for the respective time windows Δt ₁ , Δt ₂ , ..., Δt _n and/or for the respective route sections Δx ₁ , Δx ₂ , ..., Δx _n Identification of the driving style for the respective time window Δt _i and / or for the respective route section Δx _i is calculated. The characteristic values can be calculated directly while driving, in particular in real time, or it is provided to first record and save the measurement data 50 in a time sequence, and only to carry out the calculation of the characteristic values in post-processing at a later point in time. In particular, the measurement data 50 can be stored in the cloud computing infrastructure 70 or in a memory module of the user interface 49 using a software application.

Like in the 3 shown, the frequency of the characteristic values determined during a journey can be displayed in a diagram, in particular a histogram. The characteristic value index is plotted on the x-axis and the frequency of a characteristic value is plotted on the y-axis. If, with a frequency distribution of the characteristic values that range between the values 0.2 and 0.4, the maximum of the distribution is at a characteristic value of 0.3, this corresponds to a balanced driving style. A comfortable driving style is assigned to a maximum at a characteristic value of 0.1 with a frequency distribution of the characteristic values between the values 0 and 0.2. On the other hand, a maximum of 0.5 with a frequency distribution of the characteristic values between 0.2 and 0.8 defines a sporty driving style. However, this is only an exemplary representation. The values of the characteristic values can also have a different frequency distribution.

The determined characteristic values for the sequence of time windows Δt ₁ , Δt ₂ , ..., Δt _n and/or route sections Δx ₁ , Δx ₂ , ..., Δx _n can be displayed overall in a histogram, so that at the end of a journey A diagram showing the frequency of the selected driving styles on a route or the individual route sections is available. It is also conceivable that an average value or a comparable index value is created, which averages the calculated characteristic values and thus represents a Key Core Score value for a route or a route section.

In particular, the diagrams such as histograms and/or the average values of the characteristic values or key core score values can be summed up over the entire running time of a motor vehicle, so that a motor vehicle 20 can be characterized by a flag travel histogram and/or a key core score value. This can be of interest when reselling a motor vehicle 20, since this creates a criterion for the type of use of the motor vehicle 20 that goes beyond the pure mileage. For an electric vehicle or hybrid vehicle, the condition of the traction battery in particular can also be evaluated. In addition, the customer can be offered certain products or vehicle setups based on their determined sportiness index. In particular, provision can be made to use a software application installed on the customer's mobile device, for example to send push messages.

In a further development it is provided that the route or the route segment is entered on a navigation device 82 and the length of the time windows Δt ₁ , Δt ₂ , ..., Δt _n is determined depending on the respective route sections of the route or the route segment .The length of the route sections Δx ₁ , Δx ₂ , ..., Δx _n of the route can be determined based on the topography or traffic situation of the selected route. This allows the computing capacity to be used more efficiently, since on route sections with a uniform course, such as a section on a motorway, the driving style remains essentially constant and therefore continuous calculation of the driving style is not necessary.

The route or route segment can be assigned a possible index of sportiness based on local conditions and legal requirements such as speed limits or traffic density, which can be shown to the driver, for example, on the display of the navigation device 82. The evaluation of routes or route segments with a sportiness index can, for example, be stored in a database that the navigation device 82 or a software application 47 can access on the user interface 49.

In particular, the data sent to the cloud computing infrastructure 70 is cryptographically encrypted using a software application in order to prevent the determined data from being spied out. In particular, asymmetric encryption such as O-penPGP, S/MIME and cryptographic protocols such as SSH or SSL/TLS can be used as cryptographic encryption methods.

Since it may be of interest to the driver of the motor vehicle 20 to find out after driving a route in which driving style he used the route, he can use the software application 47 on the user interface 49 to view the histogram and/or the characteristic values and others View evaluation results. In particular, the user interface 49 is designed as a mobile terminal with a touchscreen as a display, in particular as a smartphone. It may be necessary that the driver or user must enter authentication to retrieve the characteristic values using the software application 47 to prove authorization. This can be, for example, a password, a numeric or alphanumeric PIN code, a signature card, a security token, a TAN procedure, a one-time PIN code or biometric features such as a fingerprint, facial recognition or iris recognition.

In a further development of the invention, it can be provided that several drivers of different vehicles compare their histograms for their vehicle or a specific route with one another and exchange information. Furthermore, the characteristic values determined about the driving style and the histograms created are of interest to the vehicle manufacturer, as this makes customer interest and customer expectations regarding the driving style of a vehicle more transparent and can therefore be taken more into account in future vehicle development. In particular, vehicle components and the chassis, which are subject to greater stress due to a sporty driving style, can be better adapted. It is also conceivable to link further services to the driving style determined, such as changing insurance tariffs depending on the driving style or reserving fast charging stations and pre-ordering new tires if driving style is sporty.

In the 4 the process steps of the method according to the invention are shown.

In a step S10, measurement data 50 is determined by means of the sensors 29 of parameters of the motor vehicle 20 in a time sequence when driving on the route or the route segment.

In a step S20, the measurement data 50 is sent to the control module 40 and/or to a cloud computing infrastructure 70 and/or a user interface 49.

In a step S30, the measurement data 50 is used by the control module 40 and/or the cloud computing infrastructure 70 and/or a user interface 49 to determine a first characteristic value for identifying the driving style in the first time window Δt ₁ and/or the first route section Δx ₁ edited.

In a step S40, the determined first characteristic value for the first time window Δt ₁ and/or the first route section Δx ₁ is saved.

In a step S50, a further characteristic value is used to identify the driving style in the respective subsequent time window Δt ₂ , ..., Δt _n and/or the respective subsequent route section Δx ₂ , ..., Δx _n when driving on the route or the route segment determined.

In a step S60, the respective determined characteristic value for the respective time window Δt ₂ , ..., Δt _n and/or the respective route section Δx ₂ , ..., Δx _n of the route or the route segment is stored.

In a step S70, a diagram such as a histogram and/or a key core score value such as an average value for the determined characteristic values is/are created while driving on the route or the route segment.

5 schematically illustrates a computer program product 200 that includes executable program code 250 configured to execute the method according to the first aspect of the present invention. The computer program product 200 is, in particular, a software application on the user interface 49 designed as a mobile radio device.

By creating a diagram such as a histogram of the selected driving styles during a route, a significantly improved analysis and evaluation of the actual driving behavior over a longer period of time is possible. In particular, a histogram of the driving styles and/or a Key Core Score value can be created for a motor vehicle during its running time, so that in addition to the mileage, the type of preferred driving styles also form a criterion for the condition of the motor vehicle. In the case of an electric vehicle or hybrid vehicle, the condition of the traction battery can be evaluated in particular. In addition, knowing a customer's preferred driving style can improve a customer-specific approach.

Reference symbols

20

motor vehicle

21

Drive system

23

Drivetrain

25

Wheels

27

Braking system

29

Sensors

30

Data communication system

40

Control module

41

processor

43

Memory module

45

Cellular module

47

Software application

49

User interface

50

Measurement data

70

Cloud computing infrastructure

80

GPS connection

82

navigation device

100

system

200

Computer program product

250

Program code

QUOTES INCLUDED IN THE DESCRIPTION

This list of 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.

Cited patent literature

• DE 102013209792 B4 [0004]
• EP 3335951 B1 [0005]
• DE 102013209787 A1 [0006]

Claims (15)

Method for classifying the driving styles in a motor vehicle (20) when driving on a route or a route segment, the motor vehicle (20) having a drive system (21), a drive train (23), wheels (25), braking systems (27), sensors (29 ), a data communication system (30) and at least one control module (40) with at least one processor (41) and a memory module (43), and wherein the route or the route segment is divided into a sequence of route sections Δx ₁ , Δx ₂ , ... , Δx _n and a sequence of time windows Δt ₁ , Δt ₂ , ..., Δt _n , whereby a time window Δt _i is assigned to a route section Δx _i or is determined independently of a route section Δx _i , with the following method steps: - Determining (S10) measurement data (50) by means of the sensors (29) of parameters of the motor vehicle (20) in a time sequence when driving on the route or the route segment; - Sending (S20) the measurement data (50) to the control module (40) and/or to a cloud computing infrastructure (70) and/or to a user interface (49); - Processing (S30) of the measurement data (50) from the control module (40) and/or the cloud computing infrastructure (70) and/or the user interface (49) to determine a first characteristic value for identifying the driving style in the first time window ( Δt ₁ ) and/or the first route section (Δx ₁ ); - Saving (S40) the determined first characteristic value for the first time window (Δt ₁ ) and/or the first route section (Δx ₁ ); - Determine (S50) a further characteristic value for identifying the driving style in the respective subsequent time window (Δt ₂ , ..., Δt _n ) and / or the respective subsequent route section (Δx ₂ , ..., Δx _n ) when driving on the route or route segment; - Saving (S60) the respectively determined characteristic value for the respective time window (Δt ₂ , ..., Δt _n ) and/or the respective route section (Δx ₂ , ... , Δx _n ) of the route or the route segment; - Creation (S70) of a diagram such as a histogram and/or a key core score value such as an average value for the determined characteristic values while driving on the route or the route segment. Procedure according to Claim 1 , where low characteristics are associated with a comfortable driving style, medium characteristics with a balanced driving style and high characteristics with a sporty driving style. Procedure according to Claim 1 or 2 , with the characteristic values moving on a scale from 0 to 1. Procedure according to one of the Claims 1 until 3 , wherein the parameters of the motor vehicle (20) include a speed and/or a lateral acceleration and/or a longitudinal acceleration and/or an accelerator pedal activity and/or a brake pedal activity and/or a torque of the drive system and/or a tire slip. Procedure according to one of the Claims 1 until 4 , wherein a GPS connection (80) is provided and the respective time window (Δt ₁ , Δt ₂ , ..., Δt _n ) and / or the respective route section (Δx ₁ , Δx ₂ , ..., Δx _n ). geographical location is assigned. Procedure according to one of the Claims 1 until 5 , wherein the route is entered on a navigation device (82) and the length of a time window (Δt ₁ , Δt ₂ , ..., Δt _n ) and / or a route section (Δx ₁ , Δx ₂ , ..., Δx _n ) is determined depending on a topography and/or a traffic situation of the route or route segment. Procedure according to one of the Claims 1 until 6 , wherein the data sent to the cloud computing infrastructure (70) is cryptographically encrypted, in particular with a software application. Procedure according to one of the Claims 1 until 7 , wherein the control module (40) is equipped with at least one radio module (45) for communication with the cloud computing infrastructure (70) and / or the user interface (49). Procedure according to one of the Claims 1 until 8th , whereby a diagram such as a histogram and/or a key core score value such as an average for the characteristic values determined on a route is created for a large number of routes and an overall diagram is created from the large number of diagrams and/or from the large number of key cores Score values an entire Key Core Score value is determined. Procedure according to one of the Claims 1 until 9 , wherein the control module (40) and/or the cloud computing infrastructure (70) and/or the user interface (49) use artificial intelligence algorithms such as, in particular, reinforcement learning and/or neural networks for determining the characteristic values for identifying the driving styles and/or or used to create a diagram. Procedure according to one of the Claims 1 until 10 , wherein the measurement data (50) determined by the sensors (29) are transmitted to the control module (40) by means of the data communication system (30). Procedure according to one of the Claims 1 until 11 , wherein the diagrams and/or the characteristic values as well as further evaluation results are displayed on the user interface (49) using a software application (47). System (100) for classifying the driving styles of a motor vehicle (20) when driving on a route or a route segment with a motor vehicle (20) which has a drive system (21), a drive train (23), wheels (25), brake systems (27) , sensors (29), a data communication system (30) and at least one control module (40) with at least one processor (41) and a memory module (43), the system (100) being designed to implement the method according to one of Claims 1 until 12 to carry out. Control module (40) of a motor vehicle (20), wherein the control module (40) comprises at least one processor (41) and a memory module (43) and is designed to use the method according to one of Claims 1 until 12 to carry out. Computer program product (200), comprising an executable program code (250) configured to implement the method according to one of Claims 1 until 12 to carry out.

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