DE102021103697A1 - METHOD, DEVICE FOR DATA PROCESSING, DEVICE AND SYSTEM - Google Patents

Abstract

The invention relates to a method for determining a manipulation of trip data (7) of a control device (3) of a vehicle (2), containing:
Obtaining the journey data (7), the journey data (7) representing a movement profile (31) of the vehicle (2) on a route driven;
Obtaining at least one position (9) of the vehicle (2), the at least one position (9) of the vehicle (2) being indicative of at least one section of the route traveled; and
Entering the travel data (7) obtained and the at least one position (9) obtained for the vehicle (2) into a machine learning algorithm (14), the machine learning algorithm (14) being set up to do this based on the movement profile (31) of the vehicle ( 2) and the at least one position (9) of the vehicle (2) obtained to determine whether the travel data (7) obtained have been manipulated.

Description

FIELD OF THE INVENTION

The present invention relates in general and in particular to a method, a device for data processing and a system for detecting manipulation of trip data of a vehicle monitoring device and to a device for transmitting trip data from a vehicle monitoring device to detect manipulation of the trip data.

BACKGROUND OF THE INVENTION

Monitoring devices for vehicles are generally known, in particular for commercial vehicles and trucks (trucks). Such control devices are also known as electronic tachographs. By law, such electronic tachographs must be provided in commercial goods transport to monitor driving and break times in trucks with a total weight of more than 7.5 t (tons). Typically, the journey data recorded in this way can be read out via a communication interface on the electronic tachograph and, for example, transferred to a storage medium (e.g. USB stick).

In known electronic tachographs, identification cards (driver cards) are inserted, on which personalized card usage data are stored. The data stored on the driver cards represent e.g. B. Information about the distance covered during the journey and information about the respective status of the driver. The status of the driver describes, for example, whether the driver is currently driving the truck, doing other work or taking a break.

The electronic tachograph is typically connected to a distance sensor ("KITAS" - Kienzle Sensor). The distance sensor determines the revolutions of the drive axle via a Hall sensor, for example, and can also calculate the driving speed and the distance covered from the number of pulses (based on a unit of time).

It is known that electronic tachographs are additionally connected to the truck's engine control unit via a truck's communication bus (e.g. CAN bus; CAN stands for "Controller Area Network") for further verification. The electronic tachograph receives additional movement information on the current driving speed of the truck from the engine control unit. This movement information can be compared in a subsequent analysis with the movement information determined by the distance sensor.

Various software programs are known for evaluating the trip data of the control device and the inserted driver card.

The journey data from the control device and the ticket inserted are typically viewed in terms of the distance travelled, the driving speed over the last few days, any position data obtained via a connected GNS system (GNS stands for "Global Navigation System"), and the entered movement statuses (e.g. Steering, interruption, work and break) evaluated, which a driver z. B enters via a control panel of the control device.

Based on this travel data z. B. Evaluations of the driving and break times that the driver has or has not complied with over the last few days.

Depending on the result of the evaluation, violations of the maximum permissible driving speed or driving and break times are identified by the control bodies (in Germany the BAG ("Federal Office for Goods Transport") and the police) and prosecuted if necessary.

Since the basis for the subsequent evaluation with regard to compliance with the maximum permissible driving speeds and driving and break times are typically the impulses from the distance sensor (distance impulses; distance signal), on the basis of which the driving speed and thus the distance covered are calculated, it is known that distance impulses be manipulated in order to manipulate journey data and thus make it more difficult to determine violations of the permissible maximum driving speed or of the driving and break time regulations. It is also known that the inserted tickets can be manipulated by making unauthorized copies.

Various methods of manipulating the distance sensors have been used for several years, such as adding electronic circuits within the distance sensor, blocking the distance signal with a short circuit and/or other electrical aids, or manipulating the distance signal with attached magnets.

The movement information coming from the engine control unit is also possibly manipulated; e.g. B. through changes to the engine control unit itself, in which emulators simulate a speed signal from the engine control unit.

It is known that the operating software of the control device is able to recognize such manipulations and to store corresponding error messages in the journey data, which are read out of the tachograph by the control bodies at a later point in time.

Likewise, in the event of discrepancies between the distance signals from the distance sensor and the engine control unit, error messages are stored in the engine control unit, which can be read out later.

However, it is known that e.g.

If the engine control unit no longer has any error messages, but the control unit may still have stored error messages, the manipulations can be detected.

However, the situation is different if the distance signal has been successfully manipulated without the error detection routines in the control device having detected this manipulation. The distance traveled/driving speed is then stored in such a way that the entries with manipulated movement states (driving and break times) may not be discovered during any checks.

Logically, the total number of kilometers driven by the vehicle, i.e. the distance actually covered, should no longer be correct if the driver enters into the electronic tachograph that he is currently taking a break, but the vehicle is actually moving in contradiction to this.

In such cases, during an inspection by the control bodies, the discrepancy between the mileage on the truck's speedometer ("Tacho") and the mileage displayed on the tachograph is noticeable.

However, it is known that system messages about the mileage that come from the engine control unit are intercepted and instead the mileage (distance traveled) of the control unit is sent to the speedometer, so that the control unit and the speedometer show the same values as a result.

It is also known that there may be direct intervention in the firmware (operating software) of the control device and/or in the firmware of the truck's control units. Once the firmware of the electronic tachograph has been manipulated, any (simulated or fictitious) movement states of the truck can be stored without these movement states having to have anything to do with the actual movement state. A check and analysis of the downloaded trip data may therefore no longer reveal any manipulations.

The manipulations by changing the firmware in the control device are typically implemented in that the movement states (driving and break times) of the truck are no longer entered by the driver, but by the manipulated firmware of the control device, for example by storing the corresponding data in the control device .

The aforementioned manipulation of control devices, control units and route signals and the resulting manipulation of driving data creates a major safety problem, which is why the control bodies monitor compliance with driving and break times to a large extent in trucks. Driver fatigue can result in potentially serious accidents.

There is therefore a great need for security on the part of the population when it comes to identifying manipulated control devices.

The object of the present invention is therefore an improved method, an improved device for data processing and an improved system for determining manipulation of journey data of a control device in a vehicle, and a device for transmitting journey data from a control device in a vehicle to determine manipulation of the journey data in order, for example, to improve the detection of manipulations.

SUMMARY OF THE INVENTION

• Erhalten der Fahrtdaten, wobei die Fahrtdaten ein Bewegungsprofil des Fahrzeugs auf einer gefahrenen Strecke repräsentieren;
• Erhalten von wenigstens einer Position des Fahrzeugs, wobei die wenigstens eine Position des Fahrzeugs indikativ ist für wenigstens einen Streckenabschnitt der gefahrenen Strecke; und
• Eingeben der erhaltenen Fahrtdaten und der erhaltenen wenigstens einen Position des Fahrzeugs in einen maschinellen Lernalgorithmus, wobei der maschinelle Lernalgorithmus dazu eingerichtet ist, basierend auf dem Bewegungsprofil des Fahrzeugs und der erhaltenen wenigstens einen Position des Fahrzeugs, zu ermitteln, ob die erhaltenen Fahrtdaten manipuliert sind.

According to a first aspect, the present invention provides a method for determining a manipulation of journey data of a control device of a vehicle, comprising:

• Obtaining the journey data, the journey data representing a movement profile of the vehicle on a driven route;
• obtaining at least one position of the vehicle, wherein the at least one position of the vehicle is indicative of at least a portion of the traveled route; and
• Entering the travel data obtained and the at least one position of the vehicle obtained into a machine learning algorithm, wherein the machine learning algorithm is configured to determine whether the travel data obtained is manipulated based on the movement profile of the vehicle and the at least one position of the vehicle obtained.

According to a second aspect, the present invention provides an apparatus for data processing, comprising an electronic circuit, the electronic circuit being set up to carry out the method according to the first aspect.

• die Fahrtdaten aus dem Kontrollgerät auszulesen, wobei die Fahrtdaten ein Bewegungsprofil des Fahrzeugs auf einer gefahrenen Strecke repräsentieren;
• eine eigene Position zu bestimmen; und
• die ausgelesenen Fahrdaten und die eigene Position an eine Vorrichtung zur Datenverarbeitung über ein Netzwerk zu übermitteln, wobei die eigene Position indikativ ist für eine Position des Fahrzeugs und die Position des Fahrzeugs indikativ ist für wenigstens einen Streckenabschnitt der gefahrenen Strecke.

According to a third aspect, the present invention provides a device for transmitting trip data from a monitoring device of a vehicle to determine manipulation of the trip data, comprising an electronic circuit, the electronic circuit being set up to:

• to read out the journey data from the monitoring device, the journey data representing a movement profile of the vehicle on a route traveled;
• to determine one's own position; and
• to transmit the driving data read out and one's own position to a device for data processing via a network, wherein one's own position is indicative of a position of the vehicle and the position of the vehicle is indicative of at least one section of the route driven.

• ein Gerät nach dem dritten Aspekt; und
• die Vorrichtung zur Datenverarbeitung, enthaltend einen elektronischen Schaltkreis, wobei der elektronische Schaltkreis dazu eingerichtet ist:
  • die Fahrtdaten zu erhalten;
  • die Position des Geräts zu erhalten; und
  • die erhaltenen Fahrtdaten und die erhaltene Position des Geräts in einen maschinellen Lernalgorithmus einzugeben, wobei der maschinelle Lernalgorithmus dazu eingerichtet ist, basierend auf dem Bewegungsprofil des Fahrzeugs und der erhaltenen Position des Geräts, zu ermitteln, ob die erhaltenen Fahrtdaten manipuliert sind.

According to a fourth aspect, the present invention provides a system for determining a manipulation of journey data of a control device of a vehicle, comprising:

• a device according to the third aspect; and
• the device for data processing, containing an electronic circuit, the electronic circuit being set up to:
  • to receive the journey data;
  • get the location of the device; and
  • enter the travel data obtained and the position of the device obtained into a machine learning algorithm, the machine learning algorithm being set up to determine whether the travel data obtained are manipulated based on the movement profile of the vehicle and the position of the device obtained.

Further aspects and features of the present invention result from the dependent claims, the accompanying drawings and the following description of embodiments.

character list

• 1 schematisch eine Ausführungsform eines Systems zur Ermittlung einer Manipulation von Fahrtdaten eines Kontrollgeräts eines Fahrzeugs veranschaulicht;
• 2 schematisch in Graphen in 2A bis 2D Ausführungsformen von Streckenabschnitten und Bewegungsprofilen veranschaulicht;
• 3 schematisch in einem Blockdiagram eine Ausführungsform eines Trainingsverfahrens für einen maschinellen Lernalgorithmus zur Ermittlung einer Manipulation von Fahrtdaten eines Kontrollgeräts eines Fahrzeugs veranschaulicht;
• 4 schematisch in einem Blockdiagram eine Ausführungsform eines Allzweckcomputers veranschaulicht; und
• 5 schematisch in einem Flussdiagram eine Ausführungsform eines Verfahrens zur Ermittlung einer Manipulation von Fahrtdaten eines Kontrollgeräts eines Fahrzeugs veranschaulicht.

Embodiments of the invention will now be described by way of example and with reference to the accompanying drawings, in which:

• 1 schematically illustrates an embodiment of a system for detecting a manipulation of journey data of a control device of a vehicle;
• 2 schematic in graphs in 2A until 2D illustrates embodiments of route segments and movement profiles;
• 3 schematically illustrates in a block diagram an embodiment of a training method for a machine learning algorithm for determining a manipulation of trip data of a control device of a vehicle;
• 4 schematically illustrates in a block diagram an embodiment of a general purpose computer; and
• 5 schematically illustrates in a flow chart an embodiment of a method for determining a manipulation of journey data of a control device of a vehicle.

DESCRIPTION OF EMBODIMENTS

In 1 1 is illustrated an embodiment in accordance with the present invention. A detailed description is preceded by general explanations of the embodiments.

As mentioned at the outset in the background of the invention, manipulations of control devices, control devices and route signals in vehicles are known, especially in trucks, whereby the recorded driving data are manipulated in order to make it more difficult to determine violations of the permissible maximum driving speed or of the driving and break time regulations. This poses a major safety problem, because serious accidents can possibly occur as a result of driver fatigue in particular. There is therefore a need for security to determine manipulations.

It was recognized that a plausibility check of the trip data from the control device based on pattern recognition in various data sources (environmental data, control unit data, etc.) using comparative data from other trips on the same route could uncover manipulation of the trip data. In particular, regardless of the underlying nature of the Manipulation (e.g. distance signal, firmware of the control device) determines whether the journey data has been manipulated.

It was also recognized that, based on anonymized case data from a large number of control devices, manipulation of security certificates and manipulation of the mileage display could also be revealed.

• Erhalten der Fahrtdaten, wobei die Fahrtdaten ein Bewegungsprofil des Fahrzeugs auf einer gefahrenen Strecke repräsentieren;
• Erhalten von wenigstens einer Position des Fahrzeugs, wobei die wenigstens eine Position des Fahrzeugs indikativ ist für wenigstens einen Streckenabschnitt der gefahrenen Strecke; und
• Eingeben der erhaltenen Fahrtdaten und der erhaltenen wenigstens einen Position des Fahrzeugs in einen maschinellen Lernalgorithmus, wobei der maschinelle Lernalgorithmus dazu eingerichtet ist, basierend auf dem Bewegungsprofil des Fahrzeugs und der erhaltenen wenigstens einen Position des Fahrzeugs, zu ermitteln, ob die erhaltenen Fahrtdaten manipuliert sind.

Therefore, some embodiments relate to a method for determining a manipulation of journey data of a control device of a vehicle, the method containing:

• Obtaining the journey data, the journey data representing a movement profile of the vehicle on a driven route;
• obtaining at least one position of the vehicle, wherein the at least one position of the vehicle is indicative of at least a portion of the traveled route; and
• Entering the travel data obtained and the at least one position of the vehicle obtained in a machine learning algorithm, the machine learning algorithm being set up to determine whether the travel data obtained are manipulated based on the movement profile of the vehicle and the at least one position of the vehicle obtained.

The method is applied in particular to trip data from a control unit of a truck or commercial vehicle, although the invention is not limited to these cases. In addition, in some embodiments, the vehicle type and/or the vehicle type is also determined or it is known, so that in general the determination of manipulation can also include the vehicle type or vehicle type, since, for example, the movement profile of a high-powered passenger car is different than that of a truck or a heavy-duty transporter .

The procedure can be carried out on site, e.g. B. in a control (e.g. by stopping) by control bodies, or in a later analysis of stored travel data.

The aim of the method is to determine whether manipulation of the trip data is likely or has taken place, although in some embodiments it is not important to determine the exact cause or the exact type of manipulation. In particular when using the method for on-site inspections, some embodiments provide for the inspection bodies to be shown a classification result which is indicative of e.g. B. an existing manipulation, a probable manipulation or no manipulation of the journey data, so that possible further steps could then be taken by the control bodies to determine the type and extent of the manipulation.

The method is carried out in particular on a computer, server or in the cloud (which includes one or more remote computers). Therefore, some embodiments relate to a device for data processing, wherein the device for data processing contains an electronic circuit that is set up to carry out the method described herein.

For this purpose, the electronic circuit of the device for data processing can have one or more processors (e.g. CPU, application processor, graphics processor, etc.), one or more memory elements (e.g. hard disk, RAM, ROM, semiconductor memory, etc.), one or more FPGAs ( "Field Programmable Gate Array"), contain one or more application-specific circuits (ASICs - "Application Specific Integrated Circuit") and/or contain typical electronic components that are configured accordingly to carry out the method. The method may be based on computer programs that include a sequence of instructions that, upon execution of the instructions, cause a computer/processor to perform the method described herein. The method can be based partly on computer programs and partly on electronic circuits.

The electronic circuitry of the device for data processing contains a communication interface for data exchange with other computers, devices, etc. via a network, whereby the data communication can be wired or wireless. The network can be a cellular network, a computer network (e.g. Internet), etc., and the electronic circuit of the device for data processing then contains appropriate hardware interfaces and implements appropriate communication protocols for data exchange.

In the method described herein, trip data from the control device is obtained.

Travel data are e.g. B. such data that are stored on the recording device and the driver card (s), such. B. a driving speed profile (driving speed over time), mileage (distance covered) and the entered driving and break times. This trip data therefore represents a movement profile of the vehicle within the recorded Period of time and therefore a movement profile of the vehicle on a route traveled by the vehicle. The movement profile is characterized in particular by the driving speed profile and the driving and break times.

The device for data processing can load the trip data from a storage medium or receive it via a communication interface.

It was recognized that if at least one position of the vehicle is known, which is indicative of at least one section of the route traveled by the vehicle, the movement profile of the vehicle can be compared with the movement profile of other vehicles that have traveled the same section. A movement profile that is characteristic of the route section, in particular a driving speed profile, can be determined on the basis of a large number of movement profiles of vehicles (a vehicle class such as trucks) on the route section.

In some embodiments, a position of the vehicle is indicative of a route section if it is determined within the period of time of the recorded journey data. In some embodiments, a position of the vehicle is indicative of a route section if the time of the position determination is shortly before or after the time period of the recorded travel data. In some embodiments, from the presence of a position of the vehicle, part of the trip data can be associated with the position of the vehicle on the one hand and an area surrounding the position can be determined on the other hand, which is then indicative of a section of the route traveled by the vehicle (e.g. the indicate the position of a gas station on a motorway, so that a segment is a segment of the motorway).

For example, the position can be determined from toll data or from position data from a possibly connected GNS system of the vehicle. However, this data is not always available.

In some embodiments, provision is made for the control bodies to be provided with a (portable) device which can be used for on-site controls and has a GNS system for position determination. The device reads the trip data from the control device on site via a data bus and then transmits its own determined position and the trip data to the device for data processing. Since in some embodiments the position is also determined directly on site during the check, it is also indicative of a section of the route traveled by the vehicle, which then corresponds to a section of road in the vicinity of the checkpoint.

• die Fahrtdaten aus dem Kontrollgerät auszulesen, wobei die Fahrtdaten ein Bewegungsprofil des Fahrzeugs auf einer gefahrenen Strecke repräsentieren;
• eine eigene Position zu bestimmen; und
• die ausgelesenen Fahrdaten und die eigene Position an eine Vorrichtung zur Datenverarbeitung über ein Netzwerk zu übermitteln, wobei die eigene Position indikativ ist für eine Position des Fahrzeugs und die Position des Fahrzeugs indikativ ist für wenigstens einen Streckenabschnitt der gefahrenen Strecke.

Therefore, some embodiments relate to a device for transmitting trip data of a control device of a vehicle to determine manipulation of the trip data, the device containing an electronic circuit that is set up to:

• to read out the journey data from the monitoring device, the journey data representing a movement profile of the vehicle on a route traveled;
• to determine one's own position; and
• to transmit the driving data read out and one's own position to a device for data processing via a network, wherein one's own position is indicative of a position of the vehicle and the position of the vehicle is indicative of at least one section of the route driven.

The electronic circuit of the device can consist of one or more processors (e.g. CPU, application processor, graphics processor, etc.), one or more memory elements (e.g. hard disk, RAM, ROM, semiconductor memory, etc.), one or more FPGAs ("Field Programmable Gate Array"), contain one or more application specific circuits (ASICs - "Application Specific Integrated Circuit") and/or contain typical electronic components that are configured accordingly to carry out the method. The method may be based on computer programs that include a sequence of instructions that, upon execution of the instructions, cause a computer/processor to perform the method described herein. The method can be based partly on computer programs and partly on electronic circuits.

The electronic circuitry of the device contains a communication interface for exchanging data with other computers, devices, mobile communication devices, etc. over a network, where the data communication can be wired or wireless. The network can be a cellular network, a computer network (e.g. Internet), etc. and the electronic circuitry of the device then contains appropriate hardware interfaces (e.g. LTE module (“Long Term Evolution”)) and implements appropriate communication protocols for data exchange. The electronic circuitry of the device may support Wi-Fi®, Bluetooth®, etc. to communicate with mobile communication devices.

The electronic circuitry of the device contains a GNS module for position determination e.g. via GPS (“Global Positioning System”) or Galileo.

The electronic circuitry of the device contains interfaces/data buses for reading out the driving data and control unit data and then implements the corresponding communication protocols.

The travel data obtained and the at least one position of the vehicle obtained are input into a machine learning algorithm, the machine learning algorithm being set up (i.e. being trained) to determine whether, based on the movement profile of the vehicle and the at least one position of the vehicle obtained the travel data received has been manipulated.

The machine learning algorithm can be based on a neural network. The machine learning algorithm may also be based on a support vector machine (SVM), logistic regression, a decision tree, or the like.

The machine learning algorithm is set up to H. he is trained to determine a classification result for the journey data received, which indicates whether the journey data received has been manipulated.

Therefore, in some embodiments, a classification result is output for the driving data obtained, the classification result being indicative of a probability of whether the driving data has been manipulated.

As mentioned above, it is intended to display the trip data and the classification result to the control bodies in some embodiments, so that possible further steps could then be initiated by the control bodies in order to determine the type and extent of the manipulation.

Therefore, in some embodiments, the electronic circuit of the device is also set up to communicate with a mobile communication device and to transmit its own position and/or the journey data read out to the mobile communication device.

In addition, in some embodiments, the electronic circuit of the device is further set up to receive a classification result for the transmitted journey data from the data processing device and to transmit the classification result to the mobile communication device.

Furthermore, in some versions, the electronic circuit of the device is set up to enable the device to be controlled using the mobile communication device (e.g. notebook, smartphone, tablet, etc.) which, for example, is in the possession of the control bodies at the time of the control.

As mentioned above, the at least one position of the vehicle that is obtained enables filtering of possible route sections of the route driven by the vehicle in some embodiments, since within a limited period of time only route sections in the vicinity of the position of the vehicle come into question. In some embodiments, each of these possible route sections has a characteristic movement profile, in particular the driving speed profile for trucks, e.g. B. the driving speed profile in a city or on a country road is different than on a highway.

The machine learning algorithm is therefore trained, based on a large number of comparison data, to classify movement profiles of a vehicle on a route/a route section that has been driven into manipulated and non-manipulated movement profiles.

The comparison data can, for example, be recorded driving data from other real vehicles, which can then be classified accordingly and used for the training. The comparison data can, for example, be trip data from training vehicles that have been driven on a large number of routes and were manipulated and/or not manipulated. The comparison data can be based, for example, on traffic simulations or other known simulation methods.

It was further recognized that data surrounding the route section can improve the accuracy of the classification result of the machine learning algorithm.

Therefore, in some embodiments, environmental data of the route section is obtained and the environmental data obtained for the route section are entered into the machine learning algorithm, with the machine learning algorithm also determining whether the received travel data has been manipulated based on the environmental data obtained for the route section.

The environmental data can be determined based on the at least one position from digital maps and/or loaded from a memory.

In some embodiments, the environmental data of the route section represent positions of parking lots, service areas, gas stations and/or toll stations.

In general, the environmental data can be retrieved by wire and/or wirelessly. The environmental data can also be received wirelessly via any radio devices that are arranged, for example, in parking lots, service areas, gas stations, toll booths and the like, but also via radio devices in other vehicles. The environmental data can, for example, be retrieved (wirelessly) by the control bodies from other vehicles that are in the vicinity of the checkpoint or, for example, drive past the checkpoint. As a result, for example, position data and/or movement profiles of other vehicles can be used to determine a manipulation.

If, in some embodiments, the movement profile of the vehicle contains break times, for example, the driving speed during the break time is practically zero and the break time can only have taken place at certain locations provided for this purpose, e.g. B. in parking lots, at service areas, at gas stations and/or at toll booths. In some embodiments, based on the at least one position and the driving speed profile, the distance to the position at which the break was taken can be determined therefrom. If the distance deviates from the actual distance to the parking lots, service areas, gas stations and/or toll booths on the route section, this could also be an indication of manipulation of the trip data. Such pattern recognition can be trained on the machine learning algorithm in some embodiments.

It was further recognized that, in principle, route data of the route section can improve the accuracy of the classification result of the machine learning algorithm.

Therefore, in some embodiments, route data of the route section are obtained and the route data of the route section obtained are input into the machine learning algorithm, wherein the machine learning algorithm further determines whether the travel data received has been manipulated based on the route data of the route section obtained.

In some embodiments, the route data of the route section represents a maximum speed profile, an elevation profile, past traffic jams and/or past traffic reports.

The road data can e.g. B. be determined based on the at least one position from digital maps and be determined by (official) traffic data platforms and / or loaded from a memory.

In principle, each route section is characterized by a predetermined maximum speed profile and a predetermined height profile, as a result of which characteristic patterns occur in the driving speed profile of a large number of vehicles in some embodiments. Starting from a checkpoint, for example, if the permissible maximum speeds are observed, periods of time with the corresponding permissible maximum speeds result in some embodiments in the journey data or the driving speed profile, with the time interval between the periods of time also being characteristic. Due to the height profile, characteristic patterns occur in some embodiments in the driving speed profile, e.g. B. a lower driving speed at high gradients, the time interval between the patterns being characteristic. For example, in some embodiments, characteristic patterns occur when braking and accelerating, so that the machine learning algorithm can be trained to recognize the presence or absence of these patterns in the driving speed profile of the driving data obtained. The same also applies in some embodiments to movement profiles in traffic jams or other traffic situations that can be determined on the basis of traffic reports (eg roadblocks, slippery roads, etc.).

It was further recognized that vehicle ECU data can improve the accuracy of the classification result of the machine learning algorithm.

Therefore, in some embodiments, controller data, which was at least partially recorded within the period of the trip data, of the vehicle is obtained and input into the machine learning algorithm, wherein the machine learning algorithm further determines whether the received trip data is manipulated based on the controller data received.

The control unit data can be obtained/read out from an engine control unit, an ABS (anti-lock braking system) control unit, an air bag control unit, a transmission control unit or the like.

In some embodiments, the electronic circuitry of the device is configured to read the vehicle's control unit data and to transmit to the device for data processing via the network.

In some specific embodiments, the control unit data contain error data, with the error data representing error messages.

It is known that control units store error messages and the error messages are stored in the form of so-called—and unchangeable—“frozen frames”. These error messages can contain, for example: the mileage, the time of the error, the driving speed, the engine speed, the oil pressure, the engine temperature, the pedal positions, etc. at the time of the error.

These error messages are generated, for example, when the engine control light comes on, the ABS has triggered, the airbag has triggered, the lighting system is faulty, the oil pressure or the engine temperature are critical, etc.

In some embodiments, pattern recognition can be further improved by comparing the error messages with the movement profile of the vehicle. For example, the occurrence of an error message due to the triggering of the ABS during a pause can be indicative of manipulated trip data.

In addition to the error messages from the control units, in some embodiments control unit data are read out that have been logged over a period of time parallel to the trip data. Telematics systems are known, in particular for trucks, which log the corresponding control unit data and, if necessary, transmit it to a server (for example the forwarding agent) in order to enable the technical condition of the vehicle fleet to be checked.

In some embodiments, such control unit data can represent engine speed, driving speed, oil pressure, engine temperature, pedal positions, fuel consumption, exhaust gas values, etc.

By comparing a large number of movement profiles and the corresponding time course of these values on a route section, the machine learning algorithm is trained in some embodiments to classify the movement profiles as manipulated and not manipulated on the basis of the control unit data.

In some embodiments, road data and the day of the week and time are obtained, the day of the week and the time being associated with the at least one position of the vehicle, the road data representing a road and a direction of travel on the route segment, the road data obtained and the day of the week and the time being be input into the machine learning algorithm and wherein the machine learning algorithm further determines whether the received trip data is manipulated based on the road data, the day of the week and the time.

The movement profile can be different in the morning than at noon, for example, due to different traffic situations, or it can be different during the week than at the weekend, so that this is also taken into account in the pattern recognition in some embodiments in order to further improve the pattern recognition.

The street and the direction of travel can be transmitted to the device, for example, when checking via the mobile communication device, which transmits the corresponding data to the device for data processing.

It was further recognized that emulation driving data could improve the accuracy of the machine learning algorithm's classification result.

• Emulationsdaten zu erhalten, wobei die Emulationsdaten ein Bewegungsprofil des Fahrzeugs emulieren;
• Testsignale basierend auf den Emulationsdaten zu erzeugen;
• die erzeugten Testsignale in das Kontrollgerät einzugeben;
• Emulations-Fahrtdaten aus dem Kontrollgerät auszulesen, wobei die Emulations-Fahrtdaten auf den eingegebenen Testsignalen basieren; und
• die ausgelesenen Emulations-Fahrdaten an die Vorrichtung zur Datenverarbeitung über das Netzwerk zu übermitteln.

Therefore, in some embodiments, the electronic circuitry of the device is further configured to:

• obtain emulation data, the emulation data emulating a motion profile of the vehicle;
• generate test signals based on the emulation data;
• input the generated test signals into the control device;
• read out emulation trip data from the control device, the emulation trip data being based on the inputted test signals; and
• to transmit the emulation driving data read out to the device for data processing via the network.

In some embodiments, the device is therefore connected to the vehicle's control unit via a corresponding data bus during an on-site inspection in order to emulate vehicle journeys while stationary and to read out the travel data recorded by the control unit, which are then transmitted to the data processing device.

In some embodiments, the control bodies generate emulation data via a mobile communication device (for example via a computer program) and transmit this to the device.

In such embodiments, the device then generates corresponding test signals which, for example, emulate distance signals from a distance sensor and pause times, which are then entered into the control device in order to emulate a trip with a predefined movement profile. From a comparison of the specified movement profile and the emulation movement profile, the classification of the machine learning algorithm can then be further improved, for example, in order to determine even minor deviations.

Accordingly, in some embodiments, emulation travel data of the vehicle's control device and emulation data are obtained, the emulation travel data being based on test signals and the test signals being based on the emulation data, the emulation data emulating a movement profile of the vehicle and the emulation travel data obtained and the emulation data obtained input to the machine learning algorithm, wherein the machine learning algorithm further determines whether the received ride data is manipulated based on the received emulation ride data and the received emulation data.

It was further recognized that test drive data could improve the accuracy of the machine learning algorithm's classification result.

• die eigenen Positionen bei einer Testfahrt des Fahrzeugs zu bestimmen und als Test-Positionsdaten an die Vorrichtung zu Datenverarbeitung zu übermitteln, wobei die eigenen Positionen indikativ für die Positionen des Fahrzeugs bei der Testfahrt sind; und

Therefore, in some embodiments, the electronic circuitry of the device is further configured to:

• to determine one's own positions during a test drive of the vehicle and to transmit them as test position data to the device for data processing, wherein one's own positions are indicative of the positions of the vehicle during the test drive; and

Read out test drive data from the control device and transmit it to the device for data processing, the test position data being associated with the test drive data.

In some embodiments, the device is therefore used on a test drive with the vehicle. In such embodiments, the device is connected to the vehicle's control device via a corresponding data bus during the test drive (or after the test drive) in order to transmit the recorded test drive data and the positions determined during the test drive to the data processing device. From a comparison of the movement profile during the test drive and the positions, the classification of the machine learning algorithm could be further improved in order to identify even minor deviations.

Accordingly, in some embodiments, test drive data from the vehicle's control device and test position data are obtained, the test position data being associated with the test drive data and representing positions during a test drive of the vehicle, the test drive data representing a movement profile of the vehicle during the test drive Represent a test drive and the test drive data obtained and the test position data obtained are entered into the machine learning algorithm and the machine learning algorithm further determines based on the test drive data received and the test position data received whether the drive data received are manipulated.

• ein Gerät wie hierin beschrieben enthält; und
• die Vorrichtung zur Datenverarbeitung enthält, welche einen elektronischen Schaltkreis enthält, wobei der elektronische Schaltkreis dazu eingerichtet ist:
  • die Fahrtdaten zu erhalten;
  • die Position des Geräts zu erhalten; und
  • die erhaltenen Fahrtdaten und die erhaltene Position des Geräts in einen maschinellen Lernalgorithmus einzugeben, wobei der maschinelle Lernalgorithmus dazu eingerichtet ist, basierend auf dem Bewegungsprofil des Fahrzeugs und der erhaltenen Position des Geräts, zu ermitteln, ob die erhaltenen Fahrtdaten manipuliert sind.

Some embodiments relate to a system, as described herein, for detecting a manipulation of trip data of a control device of a vehicle, the system:

• includes a device as described herein; and
• contains the device for data processing, which contains an electronic circuit, the electronic circuit being set up to:
  • to receive the journey data;
  • get the location of the device; and
  • enter the travel data obtained and the position of the device obtained into a machine learning algorithm, the machine learning algorithm being set up to determine whether the travel data obtained are manipulated based on the movement profile of the vehicle and the position of the device obtained.

The pattern recognition in movement profiles of vehicles using a machine learning algorithm could be used to determine manipulated travel data independently of the type of manipulation, which can solve a major security problem in some embodiments.

Coming back to 1 this illustrates an embodiment of a system 1 for determining a manipulation of journey data 7 of a control device 3 of a vehicle 2.

The vehicle 2 is here a truck (hereinafter: truck) that was stopped at a checkpoint KP by the control bodies.

The truck 2 contains the control unit 3 and control units 4, the control units 4 here comprising an engine control unit and an ABS control unit and, for the sake of simplicity, are summarized below under “the control unit 4”.

The control bodies are in possession of a device 5 and a mobile communication device 6.

The device 5 contains data buses 5a and 5b for connecting to the control device 3 or to the control device 4.

Device 5 reads trip data 7 from control device 3 via data bus 5a and control device data from control device 4 via data bus 5b.

The device 5 contains a GPS module 5d and determines its own position 9, which is indicative of the position of the truck 2.

The device 5 transmits the journey data 7, the control device data 8 and the position of the device 9 to the mobile communication device 6 via a Bluetooth interface 5c.

The device 5 transmits the journey data 7, the control device data 8 and the position of the device 9 via a mobile radio interface 5e to a base station 10, which transmits these via a network 11 to a server 12 (device for data processing).

The server 12 contains a memory 13 on which a computer program is stored which implements a (trained) machine learning algorithm 14 and is executed by one or more processors (not shown).

Based on the position 9 of the vehicle 2, the server 12 receives environmental data 15 and route data 16 from a database 17.

The travel data 7 obtained, the control unit data 8 obtained, the position 9 of the vehicle 2 , the environmental data 15 and the route data 16 are entered into the machine learning algorithm 14 .

The machine learning algorithm 14 determines a classification result 18 for the travel data 7 received, which is indicative of a probability as to whether the travel data 7 has been manipulated.

The classification result 18 is transmitted via the network to the device 5, which transmits the classification result 18 to the mobile communication device 6 of the control bodies.

2 illustrated schematically in graphs in 2A until 2D Embodiments of route sections and movement profiles.

In 2A the control point KP is shown as an example and schematically, at which the vehicle 2 from 1 controlled by the control bodies.

As referring to 1 discussed, the device 5 determines a position 9 during the check, which is indicative of the position of the vehicle 2 and thus also for the checkpoint KP.

In this exemplary embodiment, based on the position 9 of the control point KP, there are route A, route B and route C in its vicinity (illustrated by the dashed line), the vehicle 2 having driven one of these routes before the check. Position 9 is therefore indicative of at least one section of the route traveled by vehicle 2.

Route A has a parking lot 20, route B has a rest area 21 and route C has no rest area around checkpoint KP.

In the following, it is assumed for the sake of illustration that the vehicle 2 has driven the route B.

In 2 B exemplary and schematic embodiments of movement profiles on the routes AC are illustrated.

As referring to 1 discussed, the device 5 reads the trip data 7 from the control device 3 of the vehicle 2 .

The solid line in 2 B shows the real motion profile 30 of vehicle 2 on route B.

The dotted line in 2 B shows the movement profile 31 of the vehicle 2 on the route B recorded with the control device 3, which can be extracted from the trip data 7 read out.

The real movement profile 30 has a consistently higher driving speed of the vehicle 2 than the recorded movement profile 31 .

In contrast to the real movement profile 30, the recorded movement profile 31 also has a pause time between the times t1 and t2.

A first comparison movement profile 32 of route B (short-dashed line in 2 B) was determined from a large number of comparison data from a large number of vehicles and from a large number of trips with a training vehicle and represents a movement profile of the route B, which is calculated by the (trained) machine learning algorithm 14 1 classified as non-manipulated.

The first comparison movement profile 32 of the route B has a typical pause time between the times t3 and t4.

A second comparison movement profile 33 of route A (dash-dotted line in 2 B) was determined analogously to route B and is based on the (trained) machine learning algorithm 14 1 classified as non-manipulated.

The second comparison movement profile 33 of the route A has a typical pause time between the times t5 and t6.

A third comparison movement profile 34 of route C (long dashed line) was determined analogously to route B and is from the (trained) machine learning algorithm 14 1 classified as non-manipulated.

The third comparison movement profile 34 of route C has no pause time.

In order to illustrate a possible underlying principle of a pattern recognition, it is assumed below that at the time of the check at the checkpoint KP the control organs have no knowledge of the route section traveled by the vehicle 2 .

Based on the recorded movement profile 31, the machine learning algorithm 14 determines that the recorded movement profile 31 for route C is to be classified as manipulated, for example due to the pause time between t1 and t2, the consistently significantly higher driving speed and the different course of the movement profiles 31 and 34 .

The route C also has a maximum speed profile and an altitude profile (route data 16 for route C), which means that the recorded movement profile 31 cannot have originated on route C.

Based on the recorded movement profile 31, the machine learning algorithm 14 determines that the recorded movement profile 31 for route A is to be classified as manipulated. For example, due to the pause time between t1 and t2 and not between t5 and t6 and the clearly different course of the movement profiles 31 and 33.

Route A also has a maximum speed profile and an altitude profile (route data 16 for route A), which means that the recorded movement profile 31 cannot have originated on route A.

Based on the recorded movement profile 31, the machine learning algorithm 14 determines that the recorded movement profile 31 for route B is to be classified as manipulated, for example due to the pause time between t1 and t2 and not between t3 and t4 and the temporally compressed course of the recorded movement profile 31 compared to the first comparison movement profile 32.

Assuming the recorded movement profile 31 and the first comparison movement profile 32 have no pause times. Then the machine learning algorithm 14 could still classify the recorded movement profile 31 as manipulated on the route B due to the temporally compressed course of the recorded movement profile 31 compared to the first comparison movement profile 32 .

In 2C is shown schematically and by way of example how environmental data 15 improves the classification of the machine learning algorithm 14 .

The distance on the route B to the control point KP is plotted over time on the vertical axis (illustrated here as linearly increasing for the sake of illustration only).

The dotted line illustrates the distance to the control point KP based on the recorded motion profile 31. Point 35 marks the distance at which the supposed break time was taken.

The short dashed line illustrates the distance to the control point KP based on the first comparison movement profile 32. The point 36 marks the distance at which the typical pause time is taken.

Distance 37 marks the discrepancy between the two distances.

The environmental data show that there is only service area 21 on route B in the vicinity of checkpoint KP, where breaks can be taken.

The distance obtained from the environment data matches the distance at point 36.

The machine learning algorithm 14 therefore also classifies the recorded movement profile 31 as manipulated on route B based on the environmental data.

In 2D is shown schematically and by way of example how control unit data 8 could improve the classification of the machine learning algorithm 14 .

The read-out control unit data 8 have an error message 38 at time t7, which here, for example, reports the triggering of the ABS system.

However, this error message 38 occurs within the pause time between t1 and t2, so that the triggering of the ABS system at this point in time can be classified as very unlikely.

Based on error message 38, mileage readings displayed as manipulated could also be determined, since error message 38 saves the mileage reading when the error occurs.

Overall, the machine learning algorithm 14 outputs a classification result 18 which is indicative of a manipulation of the journey data 7 .

3 schematically illustrates in a block diagram an embodiment of a training method for a machine learning algorithm 14-t for determining a manipulation of trip data 7 of a control device 3 of a vehicle 2.

The machine learning algorithm 14-t is in the training phase here and is based on a neural network in this embodiment, although the invention is not limited to this case.

The machine learning algorithm 14 -t is trained with a training data set 40 .

The training data set 40 contains a large number of data sets, each data set of the large number of data sets comprising training drive data 7-t, training control device data 8-t, at least one position 9-t associated with the training drive data 7-t, training environment data 15 -t, training route data 16-t and a classification 41 ("label") which indicates whether the training trip data is manipulated or not.

The training data record 40 was determined using a large number of comparison data from a large number of vehicles and from a large number of journeys with a training vehicle, with manipulated and non-manipulated data being present.

The records (apart from the classification 41) are input into the machine learning algorithm 14-t, which based thereon outputs a classification result 18-t for each record.

The classification result 18-t and the classification 41 are entered into a loss function 42, the loss function 42 here being a cross entropy loss.

Based on a difference between the classification result 18-t and the classification 41, the weight changes 43 are output and the weights of the machine learning algorithm 14-t are updated accordingly.

After completion of the training phase, the trained machine learning algorithm 14 with trained weights is available.

4 13 schematically illustrates in a block diagram one embodiment of a general purpose computer 130.

General purpose computer 130 represents electronic circuitry with which data processing apparatus 12 and device 5 may be implemented as described herein.

The general purpose computer 130 has components 131 to 135, a GNS module 136 in the case of device 5, and a data bus 137.

Embodiments that use software, firmware, programs, or the like to perform the methods described herein may be installed on general purpose computer 130, which is then configured to suit the particular embodiment.

The general purpose computer 130 has a CPU 131 ("Central Processing Unit"), which can execute various types of procedures and methods as described herein, e.g. B. in accordance with programs stored in read-only memory ("ROM") 132, stored in memory 134 and loaded into random access memory ("RAM") 133.

The CPU 131, ROM 132, RAM 133 and memory are connected to the data bus 137.

In addition, a communication interface 135 is connected to the data bus 137, which can be set up e.g. B. for communication via a local area network (LAN), a wireless local area network (WLAN), a mobile telecommunications system (GSM, UMTS, LTE, NR, etc.), Bluetooth, infrared, etc. The communication interface implements appropriate hardware interfaces and communication protocols.

The GNS module 136 is connected to the data bus 137 and can determine a position in accordance with a global navigation system such as GPS or Galileo.

5 FIG. 12 schematically illustrates in a flow chart an embodiment of a method 200 for determining a manipulation of trip data of a control device of a vehicle, which in some embodiments runs on the general-purpose computer 130 .

At 201, trip data is obtained, where the trip data represents a motion profile of the vehicle over a driven route, as discussed herein.

At 202, at least one position of the vehicle is obtained, the at least one position of the vehicle being indicative of at least a portion of the route traveled, as discussed herein.

At 203, environmental data of the leg is obtained, as discussed herein.

At 204 route data of the route segment is obtained as discussed herein.

At 205, link controller data is obtained, as discussed herein.

1. (1) Verfahren zur Ermittlung einer Manipulation von Fahrtdaten eines Kontrollgeräts eines Fahrzeugs, enthaltend:
   • Erhalten der Fahrtdaten, wobei die Fahrtdaten ein Bewegungsprofil des Fahrzeugs auf einer gefahrenen Strecke repräsentieren;
   • Erhalten von wenigstens einer Position des Fahrzeugs, wobei die wenigstens eine Position des Fahrzeugs indikativ ist für wenigstens einen Streckenabschnitt der gefahrenen Strecke; und
   • Eingeben der erhaltenen Fahrtdaten und der erhaltenen wenigstens einen Position des Fahrzeugs in einen maschinellen Lernalgorithmus, wobei der maschinelle Lernalgorithmus dazu eingerichtet ist, basierend auf dem Bewegungsprofil des Fahrzeugs und der erhaltenen wenigstens einen Position des Fahrzeugs, zu ermitteln, ob die erhaltenen Fahrtdaten manipuliert sind.
2. (2) Verfahren nach (1), weiter enthaltend:
   • Erhalten von Umgebungsdaten des Streckenabschnitts; und
   • Eingeben der erhaltenen Umgebungsdaten des Streckenabschnitts in den maschinellen Lernalgorithmus, wobei der maschinelle Lernalgorithmus weiter basierend auf den erhaltenen Umgebungsdaten des Streckenabschnitts ermittelt, ob die erhaltenen Fahrtdaten manipuliert sind.
3. (3) Verfahren nach (2), wobei die Umgebungsdaten des Streckenabschnitts Positionen von Parkplätzen, Raststätten, Tankstellen und/oder Mautstellen repräsentieren und/oder Positionsdaten von anderen Fahrzeugen aufweisen.
4. (4) Verfahren nach einem von (1) bis (3), weiter enthaltend:
   • Erhalten von Streckendaten des Streckenabschnitts; und
   • Eingeben der erhaltenen Streckendaten des Streckenabschnitts in den maschinellen Lernalgorithmus, wobei der maschinelle Lernalgorithmus weiter basierend auf den erhaltenen Streckendaten des Streckenabschnitts ermittelt, ob die erhaltenen Fahrtdaten manipuliert sind.
5. (5) Verfahren nach (4), wobei die Streckendaten des Streckenabschnitts ein Höchstgeschwindigkeitsprofil, ein Höhenprofil, zurückliegende Verkehrsstaus und/oder zurückliegende Verkehrsmeldungen repräsentieren.
6. (6) Verfahren nach einem von (1) bis (5), weiter enthaltend:
   • Erhalten von Steuergerätedaten des Fahrzeugs, wobei die Steuergerätedaten zumindest teilweise innerhalb des Zeitraums der Fahrtdaten aufgenommen wurden; und
   • Eingeben der erhaltenen Steuergerätedaten in den maschinellen Lernalgorithmus, wobei der maschinelle Lernalgorithmus weiter basierend auf den erhaltenen Steuergerätedaten ermittelt, ob die erhaltenen Fahrtdaten manipuliert sind.
7. (7) Verfahren nach (6), wobei die Steuergerätedaten Fehlerdaten enthalten, wobei die Fehlerdaten Fehlermeldungen repräsentieren.
8. (8) Verfahren nach einem von (1) bis (7), weiter enthaltend:
   • Erhalten von Straßendaten und Wochentag und Uhrzeit, wobei der Wochentag und die Uhrzeit mit der wenigstens einen Position des Fahrzeugs assoziiert sind, wobei die Straßendaten eine Straße und eine Fahrtrichtung auf dem Streckenabschnitt repräsentieren;
   • Eingeben der erhaltenen Straßendaten und des Wochentags und der Uhrzeit in den maschinellen Lernalgorithmus, wobei der maschinelle Lernalgorithmus weiter basierend auf den Straßendaten, dem Wochentag und der Uhrzeit ermittelt, ob die erhaltenen Fahrtdaten manipuliert sind.
9. (9) Verfahren nach einem von (1) bis (8), weiter enthaltend:
   • Erhalten von Emulations-Fahrtdaten des Kontrollgeräts des Fahrzeugs und von Emulationsdaten, wobei die Emulations-Fahrtdaten auf Testsignalen basieren und die Testsignale auf den Emulationsdaten basieren, wobei die Emulationsdaten ein Bewegungsprofil des Fahrzeugs emulieren; und
   • Eingeben der erhaltenen Emulations-Fahrtdaten und der erhaltenen Emulationsdaten in den maschinellen Lernalgorithmus, wobei der maschinelle Lernalgorithmus weiter basierend auf den erhaltenen Emulations-Fahrtdaten und der erhaltenen Emulationsdaten ermittelt, ob die erhaltenen Fahrtdaten manipuliert sind.
10. (10) Verfahren nach einem von (1) bis (9), weiter enthaltend:
    • Erhalten von Test-Fahrtdaten des Kontrollgeräts des Fahrzeugs und von Test-Positionsdaten, wobei die Test-Positionsdaten mit den Test-Fahrtdaten assoziiert sind und Positionen bei einer Testfahrt des Fahrzeugs repräsentieren, wobei die Test-Fahrtdaten ein Bewegungsprofil des Fahrzeugs bei der Testfahrt repräsentieren; und
    • Eingeben der erhaltenen Test-Fahrtdaten und der erhaltenen Test-Positionsdaten in den maschinellen Lernalgorithmus, wobei der maschinelle Lernalgorithmus weiter basierend auf den erhaltenen der erhaltenen Test-Fahrtdaten und der erhaltenen Test-Positionsdaten ermittelt, ob die erhaltenen Fahrtdaten manipuliert sind.
11. (11) Verfahren nach einem von (1) bis (10), weiter enthaltend:
    • Ausgeben eines Klassifizierungsergebnisses für die erhaltenen Fahrtdaten, wobei das Klassifizierungsergebnis indikativ für eine Wahrscheinlichkeit ist, ob die Fahrdaten manipuliert sind.
12. (12) Vorrichtung zur Datenverarbeitung, enthaltend einen elektronischen Schaltkreis, wobei der elektronische Schaltkreis dazu eingerichtet ist, das Verfahren nach einem von (1) bis (11) auszuführen.
13. (13) Gerät zur Übermittlung von Fahrtdaten eines Kontrollgeräts eines Fahrzeugs zur Ermittlung einer Manipulation der Fahrtdaten, enthaltend einen elektronischen Schaltkreis, wobei der elektronische Schaltkreis dazu eingerichtet ist:
    • die Fahrtdaten aus dem Kontrollgerät auszulesen, wobei die Fahrtdaten ein Bewegungsprofil des Fahrzeugs auf einer gefahrenen Strecke repräsentieren;
    • eine eigene Position zu bestimmen; und
    • die ausgelesenen Fahrdaten und die eigene Position an eine Vorrichtung zur Datenverarbeitung über ein Netzwerk zu übermitteln, wobei die eigene Position indikativ ist für eine Position des Fahrzeugs und die Position des Fahrzeugs indikativ ist für wenigstens einen Streckenabschnitt der gefahrenen Strecke.
14. (14) Gerät nach (13), wobei der elektronische Schaltkreis weiter dazu eingerichtet ist:
    • mit einem mobilen Kommunikationsgerät zu kommunizieren; und
    • die eigene Position und/oder die ausgelesenen Fahrtdaten an das mobile Kommunikationsgerät zu übermitteln.
15. (15) Gerät nach (13) oder (14), wobei der elektronische Schaltkreis weiter dazu eingerichtet ist:
    • Emulationsdaten zu erhalten, wobei die Emulationsdaten ein Bewegungsprofil des Fahrzeugs emulieren;
    • Testsignale basierend auf den Emulationsdaten zu erzeugen;
    • die erzeugten Testsignale in das Kontrollgerät einzugeben;
    • Emulations-Fahrtdaten aus dem Kontrollgerät auszulesen, wobei die Emulations-Fahrtdaten auf den eingegebenen Testsignalen basieren; und
    • die ausgelesenen Emulations-Fahrdaten an die Vorrichtung zur Datenverarbeitung über das Netzwerk zu übermitteln.
16. (16) Gerät nach einem von (13) bis (15), wobei der elektronische Schaltkreis weiter dazu eingerichtet ist:
    • die eigenen Positionen bei einer Testfahrt des Fahrzeugs zu bestimmen und als Test-Positionsdaten an die Vorrichtung zu Datenverarbeitung zu übermitteln, wobei die eigenen Positionen indikativ für die Positionen des Fahrzeugs bei der Testfahrt sind; und
    • Test-Fahrtdaten aus dem Kontrollgerät auszulesen und an die Vorrichtung zur Datenverarbeitung zu übermitteln, wobei die Test-Positionsdaten mit den Test-Fahrtdaten assoziiert sind.
17. (17) Gerät nach (14), wobei der elektronische Schaltkreis weiter dazu eingerichtet ist ein Klassifizierungsergebnis für die übermittelten Fahrtdaten von der Vorrichtung zur Datenverarbeitung zu erhalten und an das Klassifizierungsergebnis an das mobile Kommunikationsgerät zu übermitteln.
18. (18) Gerät nach einem von (13) bis (17), wobei der elektronische Schaltkreis weiter dazu eingerichtet ist:
    • Steuergerätedaten des Fahrzeugs auszulesen; und
    • die ausgelesenen Steuergerätedaten an die Vorrichtung zur Datenverarbeitung über das Netzwerk zu übermitteln.
19. (19) System zur Ermittlung einer Manipulation von Fahrtdaten eines Kontrollgeräts eines Fahrzeugs, enthaltend:
    • ein Gerät nach einem von (13) bis (18); und
    • die Vorrichtung zur Datenverarbeitung, enthaltend einen elektronischen Schaltkreis, wobei der elektronische Schaltkreis dazu eingerichtet ist:
      • die Fahrtdaten zu erhalten;
      • die Position des Geräts zu erhalten; und
      • die erhaltenen Fahrtdaten und die erhaltene Position des Geräts in einen maschinellen Lernalgorithmus einzugeben, wobei der maschinelle Lernalgorithmus dazu eingerichtet ist, basierend auf dem Bewegungsprofil des Fahrzeugs und der erhaltenen Position des Geräts, zu ermitteln, ob die erhaltenen Fahrtdaten manipuliert sind.

At 206, the travel data obtained, the at least one position of the vehicle obtained, the environmental data obtained, the route data obtained and the control unit data obtained are entered into a machine learning algorithm, the machine learning algorithm being set up for this purpose, based on the movement profile of the vehicle, the received at least one position of the vehicle, the received environmental data, the received route data and the received control unit data to determine whether the received travel data is manipulated The present technology can also be configured as described below:

1. (1) Method for determining a manipulation of journey data of a recording device of a vehicle, containing:
   • Obtaining the journey data, the journey data representing a movement profile of the vehicle on a driven route;
   • obtaining at least one position of the vehicle, wherein the at least one position of the vehicle is indicative of at least a portion of the traveled route; and
   • Entering the travel data obtained and the at least one position of the vehicle obtained in a machine learning algorithm, the machine learning algorithm being set up to determine whether the travel data obtained are manipulated based on the movement profile of the vehicle and the at least one position of the vehicle obtained.
2. (2) Method according to (1), further containing:
   • obtaining environmental data of the route section; and
   • Inputting the received environmental data of the route section into the machine learning algorithm, the machine learning algorithm further determining whether the received travel data has been manipulated based on the received environmental data of the route section.
3. (3) Method according to (2), wherein the environmental data of the route section represent positions of parking lots, service areas, gas stations and/or toll booths and/or have position data from other vehicles.
4. (4) Method according to one of (1) to (3), further containing:
   • obtaining route data of the route section; and
   • Entering the received route data of the route section in the machine learning algorithm, wherein the machine learning algorithm determines further based on the received route data of the route section whether the received travel data has been manipulated.
5. (5) Method according to (4), the route data of the route section representing a maximum speed profile, a height profile, past traffic jams and/or past traffic reports.
6. (6) Method according to one of (1) to (5), further containing:
   • Obtaining controller data of the vehicle, wherein the controller data was recorded at least partially within the time period of the trip data; and
   • Entering the control unit data obtained into the machine learning algorithm, the machine learning algorithm being further based based on the control unit data received, determines whether the travel data received has been manipulated.
7. (7) Method according to (6), the control unit data containing error data, the error data representing error messages.
8. (8) Method according to one of (1) to (7), further containing:
   • obtaining road data and day and time of week, the day of week and time associated with the at least one location of the vehicle, the road data representing a road and a direction of travel on the route segment;
   • Inputting the obtained road data and the day of the week and time into the machine learning algorithm, wherein the machine learning algorithm further determines whether the obtained driving data is manipulated based on the road data, the day of the week and the time.
9. (9) Method according to one of (1) to (8), further containing:
   • Obtaining emulation trip data of the vehicle's control device and emulation data, the emulation trip data being based on test signals and the test signals being based on the emulation data, the emulation data emulating a movement profile of the vehicle; and
   • inputting the received emulation trip data and the received emulation data into the machine learning algorithm, wherein the machine learning algorithm further determines whether the received trip data is manipulated based on the received emulation trip data and the received emulation data.
10. (10) Method according to one of (1) to (9), further containing:
    • Obtaining test drive data from the vehicle's control device and test position data, the test position data being associated with the test drive data and representing positions during a test drive of the vehicle, the test drive data representing a movement profile of the vehicle during the test drive; and
    • Inputting the received test drive data and the received test position data into the machine learning algorithm, wherein the machine learning algorithm further determines whether the received drive data is manipulated based on the received test drive data and the received test position data.
11. (11) Method according to one of (1) to (10), further containing:
    • Outputting a classification result for the driving data obtained, the classification result being indicative of a probability of whether the driving data has been manipulated.
12. (12) Device for data processing, containing an electronic circuit, wherein the electronic circuit is set up to carry out the method according to one of (1) to (11).
13. (13) Device for transmitting trip data from a vehicle's monitoring device to detect manipulation of the trip data, containing an electronic circuit, the electronic circuit being set up to:
    • to read out the journey data from the monitoring device, the journey data representing a movement profile of the vehicle on a route traveled;
    • to determine one's own position; and
    • to transmit the driving data read out and one's own position to a device for data processing via a network, wherein one's own position is indicative of a position of the vehicle and the position of the vehicle is indicative of at least one section of the route driven.
14. (14) Device according to (13), wherein the electronic circuit is further arranged to:
    • communicate with a mobile communication device; and
    • transmit your own position and/or the travel data read out to the mobile communication device.
15. (15) Device according to (13) or (14), wherein the electronic circuit is further adapted to:
    • obtain emulation data, the emulation data emulating a motion profile of the vehicle;
    • generate test signals based on the emulation data;
    • input the generated test signals into the control device;
    • read out emulation trip data from the control device, the emulation trip data being based on the inputted test signals; and
    • to transmit the emulation driving data read out to the device for data processing via the network.
16. (16) Device according to one of (13) to (15), wherein the electronic circuit is further arranged to:
    • to determine one's own positions during a test drive of the vehicle and to transmit them as test position data to the device for data processing, wherein one's own positions are indicative of the positions of the vehicle during the test drive; and
    • Read out test drive data from the control device and transmit it to the device for data processing, the test position data being associated with the test drive data.
17. (17) Device according to (14), wherein the electronic circuit is further set up to receive a classification result for the transmitted travel data from the data processing device and to transmit the classification result to the mobile communication device.
18. (18) Device according to one of (13) to (17), wherein the electronic circuit is further arranged to:
    • read the vehicle's control unit data; and
    • to transmit the read-out control unit data to the device for data processing via the network.
19. (19) System for detecting a manipulation of journey data of a recording device of a vehicle, containing:
    • a device according to any one of (13) to (18); and
    • the device for data processing, containing an electronic circuit, the electronic circuit being set up to:
      • to receive the journey data;
      • get the location of the device; and
      • enter the travel data obtained and the position of the device obtained into a machine learning algorithm, the machine learning algorithm being set up to determine whether the travel data obtained are manipulated based on the movement profile of the vehicle and the position of the device obtained.

Claims (10)

Method for determining a manipulation of trip data (7) of a control device (3) of a vehicle (2), comprising: Obtaining the journey data (7), the journey data (7) representing a movement profile (31) of the vehicle (2) on a route driven; Obtaining at least one position (9) of the vehicle (2), the at least one position (9) of the vehicle (2) being indicative of at least one section of the route traveled; and Entering the travel data (7) obtained and the at least one position (9) obtained for the vehicle (2) into a machine learning algorithm (14), the machine learning algorithm (14) being set up to do this based on the movement profile (31) of the vehicle ( 2) and the at least one position (9) of the vehicle (2) obtained to determine whether the travel data (7) obtained have been manipulated. procedure after claim 1 , further comprising: obtaining environmental data (15) of the route section; and entering the received environmental data (15) of the route section into the machine learning algorithm (14), wherein the machine learning algorithm (14) further determines based on the received environmental data (15) of the route section whether the received travel data (7) are manipulated. procedure after claim 2 , wherein the environmental data (15) of the route section represent positions of parking lots (20), service areas (21), gas stations and/or toll booths and/or have position data from other vehicles. Method according to one of the preceding claims, further comprising: Obtaining route data (16) of the route section; and Entering the received route data (16) of the route section in the machine learning algorithm (14), the machine learning algorithm (14) further based on the received route data (16) of the route section determines whether the received travel data (7) are manipulated. procedure after claim 4 , wherein the route data (16) of the route section represents a maximum speed profile, a height profile, past traffic jams and/or past traffic reports. Method according to one of the preceding claims, further comprising: obtaining control unit data (8) of the vehicle (2), the control unit data (8) being recorded at least partially within the time period of the trip data (7); and entering the received control unit data (8) in the machine learning algorithm (14), wherein the Machine learning algorithm (14) further based on the received control unit data (8) determines whether the received travel data (7) are manipulated. procedure after claim 6 , wherein the control unit data (8) contain error data, the error data representing error messages (38). Device for data processing (12) comprising an electronic circuit, the electronic circuit being arranged to carry out the method according to any one of the preceding claims. Device (5) for transmitting trip data (7) of a control device of a vehicle (2) to determine a manipulation of the trip data (7), comprising an electronic circuit, the electronic circuit being set up to: to read out the journey data (7) from the monitoring device (3), the journey data (7) representing a movement profile (31) of the vehicle (2) on a route traveled; to determine one's own position (9); and to transmit the read driving data (7) and one's own position (9) to a device for data processing (12) via a network (11), wherein one's own position (9) is indicative of a position (9) of the vehicle (2) and the position (9) of the vehicle (2) is indicative of at least a portion of the route traveled. System (1) for determining a manipulation of journey data (7) of a control device (3) of a vehicle, comprising: a device (5). claim 9 ; and the data processing device (12) containing an electronic circuit, the electronic circuit being arranged to: obtain the journey data (7); to obtain the position (9) of the device (5); and to enter the travel data (7) obtained and the position (9) obtained from the device (5) into a machine learning algorithm (14), wherein the machine learning algorithm (14) is set up based on the movement profile (31) of the vehicle (2nd ) and the received position (9) of the device (5) to determine whether the received travel data (7) are manipulated.

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