Method for operating a cloud-based system in a vehicle

Abstract

The invention relates to a method for operating a cloud-based system in a vehicle, wherein the system performs safety-related tasks, whose execution depends on a communication between the vehicle and a cloud-based device, and wherein a deterioration of a communication quality between the vehicle and the cloud-based device is predicted, characterized in that the point in time of execution of at least one of the safety-related tasks is dynamically adapted to cope with the predicted deterioration of the communication quality.

Description

Technical Field

[0001] The present invention relates to a method and apparatus for running a cloud-based system in a vehicle, a system, a computer program, and a computer-readable storage medium. Background Technology

[0002] The use of cloud-based systems in future vehicle architectures enables improved diagnostics and safety functions. Known methods include cloud-based diagnostics, fault recovery, and the provision of safety-related information. Currently, reliability is ensured through a watchdog mechanism with fixed test intervals. However, if an interruption occurs during the test interval, such as a connectivity loss due to a signal blind spot, it often leads to unnecessary safety shutdowns.

[0003] SE 1851397 A1 describes adaptive behavior of embedded systems for improving robustness against relative communication failures. Summary of the Invention

[0004] In this context, the present invention provides a method having the features of claim 1, a control unit for a vehicle having the features of the parallel claims, a device for the exterior of a vehicle, a system, a computer program, and a computer-readable medium.

[0005] The present invention relates to operating a cloud-based system in a vehicle. This system performs various tasks, at least partially categorized as safety-related. Successful execution of these tasks depends on reliable communication between the vehicle and the cloud-based device. Interruption or impairment of this communication could lead to significant security risks.

[0006] A cloud-based system can be understood as a system that at least partially accesses the resources of a cloud-based device. It includes both components located within a vehicle, such as those that can collect data and send it to the cloud-based device, and the cloud-based device itself. Cloud-based devices can take various forms, including one or more servers interconnected via a network for processing and storing data. Both the vehicle and the cloud-based device can contain software components necessary for performing safety-related tasks. Interaction between the vehicle and the cloud-based device occurs via a communication connection that ensures stable and, as far as possible, latency-free data transmission.

[0007] Therefore, reliable communication is essential. One aspect of this method is thus predicting potential impairments in communication quality. This prediction can be based on the analysis of data, where the data source and analysis method can influence the accuracy of the prediction. This data analysis can be performed at different locations within the system.

[0008] The method is characterized in that the execution time of at least one of the security-related tasks is dynamically adapted to the predicted communication quality. This dynamic adaptation aims to increase the probability of successful task completion by selecting the execution time such that the expected communication quality is sufficiently good.

[0009] Dynamic adaptation at execution time can include various strategies. The selection of the optimal strategy depends on various factors, such as the accuracy of the prediction, the importance of the task to be processed, and available resources. To improve system reliability, redundant systems or mechanisms can be implemented, which either transition the system to a safe state in the event of a functional failure or reduce the amount of data transmitted to cloud devices. This redundancy and fault tolerance improve the system's robustness and minimize the risk of security incidents that may arise from communication failures or impairments.

[0010] This method significantly improves the reliability and security of cloud-based systems in vehicles. Dynamic adaptation at execution time minimizes potential functional failures and security risks caused by communication outages or impairments. This results in improved system availability and robustness.

[0011] Other advantages are derived from the dependent claims.

[0012] In a preferred embodiment, it is specified that the dynamic adaptation of the execution time of at least one security-related task is achieved by advancing the execution time. This means that once a loss of communication quality is predicted, the task processing time is proactively moved forward, rather than being placed within a fixed interval or postponed.

[0013] If the predicted impairment of communication quality will occur in the near future, and communication failure during the execution of security-related tasks could have serious consequences, then advancing the execution time is particularly advantageous. By triggering the task earlier, it is ensured that it can still be completed within a time window with sufficiently good communication quality. Therefore, the risk of functional failures and security incidents is minimized.

[0014] For example, consider diagnostics for a braking system. If a connection interruption is predicted shortly before the planned diagnostic time, this method allows for earlier diagnostics. If the diagnostics are completed before the connection interruption begins, any potential safety risks arising from the diagnostic interruption will be disregarded.

[0015] By implementing this advance approach, system reliability is improved, and the probability of safe operation is enhanced. Executing tasks earlier under favorable communication conditions minimizes the risk of errors and improves resource utilization efficiency. This leads to increased system availability, which is particularly important for safety-related functions in vehicles.

[0016] An alternative implementation of this method specifies that dynamic adaptation of the execution time of at least one security-related task is achieved by delaying that execution time. Conversely, in this case, the task processing time is postponed when a loss of communication quality is anticipated.

[0017] If the predicted duration of the communication quality impairment is only short and the security-related task does not need to be performed immediately, then postponing the execution time is particularly advantageous. By shifting the execution time, the task is moved to a period of time when the communication quality is expected to be sufficiently good again. This method avoids unnecessary interruptions or errors that may result from the impairment of communication quality.

[0018] For example, consider periodically transmitting driving data to a cloud-based fleet management platform. If a temporary impairment in communication quality is anticipated (e.g., due to signal blind spots), the transmission of driving data can be postponed for the predicted duration of the impairment. In this way, data loss is avoided and the quality of data transmission is ensured.

[0019] By delaying the process, security-related tasks are ensured to be executed only when communication quality is sufficiently good. This method not only avoids data loss and errors but also helps optimize resource utilization by avoiding unnecessary communication attempts during compromised environments. This results in improved system reliability, efficiency, and robustness.

[0020] In a particularly advantageous embodiment of this method, safety-related tasks are accomplished through diagnostic testing of vehicle components. Diagnostic testing here includes systematically examining the condition and functionality of specific components within the vehicle. These tests are used to identify errors early and prevent malfunctions. The results of these diagnostic tests can be used not only for preventative maintenance but also for troubleshooting.

[0021] The execution of diagnostic tests is often time-critical and requires reliable communication with cloud-based systems, especially when complex evaluations or data transfers are required. Dynamic adaptation to execution time points, as described in the previously outlined implementation, is particularly important for diagnostic tests, enabling reliable test execution even when communication quality is temporarily compromised.

[0022] An example of this type of diagnostic testing is examining sensor data from the braking system. During this process, measurements from various sensors are evaluated to determine the state of the braking system. Communication failures during such testing can lead to incorrect diagnoses and potentially serious safety risks. By dynamically adapting execution time points as applied in this method, reliable and uninterrupted braking system diagnostics can be ensured. This results in improved vehicle safety and reliability, and avoids incorrect diagnoses.

[0023] One specific implementation of this method focuses on situations where the predicted impairment of communication quality represents a complete connection failure. Such a connection failure signifies a complete breakdown of the communication connection between the vehicle and the cloud-based device. This could lead to a complete interruption of data transmission and affect or prevent the operation of safety-related functions within the vehicle.

[0024] In this context, it is particularly important to dynamically adapt the execution time of security-related tasks to ensure safe and reliable operation. By predicting connection interruptions, this method can adapt the execution time of critical tasks so that they complete before the interruption begins, or at least provide at least one last known data state before communication is lost. This prevents data loss and minimizes the risks associated with communication failures.

[0025] For example, consider an autonomous truck that periodically transmits its location to a fleet management system in the cloud. If a connection interruption is anticipated, such as due to entering a tunnel, the method can perform the location data transmission before the interruption begins. This ensures that at least one last known location (Standort) is available before the connection interruption. Therefore, the fleet manager obtains the current data status in real-time tracking before the interruption.

[0026] In a preferred embodiment of this method, the prediction of communication quality impairment is based on a Quality of Service (QoS) map. In this context, a QoS map is a map (kart) containing information about the expected quality of communication connections at different locations and times. This information can be based, for example, on historical data, measurements, or other prediction models. The QoS map shows a temporal and spatial representation of the expected communication quality and enables more accurate prediction of impairment.

[0027] The advantages of using QoS graphs compared to other prediction methods lie in their better overview and easier integration into the methodology. QoS graphs provide a structured representation of the predicted communication quality, which can be easily evaluated and integrated into dynamic adaptations at execution time. This simplifies the implementation of the method and improves the reliability of the predictions.

[0028] One example of applying QoS maps is monitoring autonomous vehicles moving in areas with known poor mobile wireless connectivity. The corresponding QoS map can represent areas with poor mobile wireless connectivity, allowing the method to adapt the timing of critical tasks, such as the transmission of location data or the execution of security diagnostics, to areas with good connectivity.

[0029] Another implementation of this method uses a machine learning model to predict impairments in communication quality. In this context, a machine learning model is a computer-supported system that learns from data and can make predictions. The model is trained using a large amount of data containing information about communication quality, such as signal strength, latency, historical connection interruptions, weather data, or information about the vehicle's environment.

[0030] The advantage of using machine learning models over other prediction methods lies in their higher accuracy and adaptability. These models can identify the complex relationships between various factors and communication quality, thus making more accurate predictions. This is especially important when communication quality is influenced by multiple factors and changes dynamically.

[0031] For example, suppose the model has been trained using historical data on connectivity interruptions in a specific area. When a vehicle is in such an area, the model can predict the probability of connectivity interruption more accurately than methods based solely on current signals. This enables more precise and efficient dynamic adaptation to the timing of safety-related task execution.

[0032] Another preferred embodiment of the method is characterized by defining a maximum permissible execution time period (Fault Tolerance Time Period, FTTI) for the at least one security-related task. This FTTI represents a pre-defined time window within which the execution of the corresponding task must begin and / or complete. The setting of the time window takes into account the need for rapid response and tolerance for potential interference or delays.

[0033] The specific duration of the FTTI is predetermined, for example, based on a lookup table or according to various factors such as task type, security requirements, and the expected conditions of the communication connection. Shorter FTTIs are chosen for particularly time-critical tasks, while longer FTTIs may be selected for less time-critical applications. The methods used to determine the specific FTTI duration will be described in detail in a separate paragraph.

[0034] The key point is that, as described above, dynamic adaptation of execution time both begins and ends within the pre-defined FTTI. Therefore, the algorithm for dynamic adaptation is designed to always consider the time constraints of the FTTI and prevent execution outside this time window. Thus, dynamic adaptation is used to ensure that the task executes within the pre-given FTTI.

[0035] Consider an example: vehicles must periodically transmit their location data to a fleet management system. An FTTI (Flexible Time Interval) is defined for this process, for example, a one-second FTTI. If poor radio signal reception is predicted, the algorithm dynamically adapts the transmission time to ensure interference-free reception as much as possible, ensuring the transmission process starts and ends within a pre-defined one-second window. By defining the FTTI and limiting the dynamic adaptation to this time window, the system's reliability, predictability, and security are significantly improved. This ensures adherence to time-critical requirements and minimizes the risk of functional failures and safety incidents.

[0036] In a particularly preferred embodiment, the maximum permissible execution time period (FTTI) is adapted in a situation-dependent manner. Unlike a fixed FTTI setting, in this embodiment, the duration of the FTTI is dynamically adapted according to the corresponding driving situation. This allows the system to be optimally coordinated with current conditions and improves the robustness and efficiency of the method.

[0037] FTTI's context-dependent adaptation takes into account various factors that may affect communication reliability and the need for rapid response. These factors may include, for example: - Weather conditions: Poor visibility or heavy rainfall may affect communication quality and may require a shorter FTTI. - Traffic conditions: High traffic volume or congestion can lead to increased delays and connectivity problems, and also justifies the need for a shorter FTTI. - Vehicle status: Critical vehicle status, such as an impending brake failure, may require a shorter FTTI in order to respond quickly to critical situations. - Wireless network coverage: The quality of wireless network coverage may vary in different locations. Areas with poor coverage may require a shorter FTTI.

[0038] The methods used to adapt FTTI in a context-dependent manner can be very different. They can be based, for example, on complex rule logic that takes into account various factors, or they can use machine learning models that determine the optimal FTTI duration based on historical data and real-time information.

[0039] In a preferred embodiment of the method, dynamic adaptation at execution time is implemented to include adapting at least one vehicle behavior. This adaptation of vehicle behavior is intended to improve or at least maintain communication availability during the execution of the at least one safety-related task, and thus address predicted impairments in communication quality.

[0040] Here, vehicle behavior is understood as the sum of all parameters that affect vehicle behavior and can be actively changed. Examples of such parameters include speed, route, vehicle position, or the configuration of vehicle components. Therefore, vehicle behavior adaptation is used as an auxiliary measure to improve communication quality.

[0041] The choice of vehicle behavior to be adapted depends on various factors, such as the type of predicted impairment in communication quality, the type of safety-related task to be performed, and environmental conditions. For example, if a connection interruption due to a signal blind spot is predicted, the vehicle can adapt its route to enter an area with better wireless connectivity. In adverse weather conditions, speed can be reduced to improve communication reliability.

[0042] Consider, for example, an autonomous vehicle that periodically transmits its location data to a cloud-based navigation system. If the system predicts a decline in communication quality, for example, due to an upcoming tunnel crossing, the vehicle can reduce its speed and / or seek a more favorable location for data transmission. Through this adaptation of driving behavior, the probability of successful data transmission is increased.

[0043] The aforementioned advantages also apply in a corresponding manner to control units for vehicles, particularly vehicle control units used to run cloud-based systems. This control unit is configured to perform at least one step of a method according to one of the previously described embodiments. Here, the control unit refers to an electronic unit that controls and monitors various functions of the vehicle. Such a control unit can be, for example, an electronic control device (ECU) that controls various sensors and actuators in the vehicle. The control unit may include one or more processors, memories, and interfaces. However, the specific configuration of this control unit is not the subject of this embodiment.

[0044] A core feature of this embodiment is the ability of the control unit to execute at least one step of the method. This means that the control unit can predict the degradation of communication quality, dynamically adapt to the timing of safety-related tasks, and execute the corresponding tasks. The control unit can communicate with various sensors and actuators within the vehicle to obtain information about the vehicle's status and environment. It can also communicate with cloud-based devices to exchange data and perform the tasks. Here, the control unit interacts with various systems within the vehicle, such as navigation systems, braking systems, or engine control units.

[0045] The aforementioned advantages also apply in a corresponding manner to a vehicle-exterior, particularly cloud-based, device for running a cloud-based system within the vehicle. This device communicates with a control unit located within the vehicle and is configured to perform at least one step of a method according to one of the previously described embodiments. A cloud-based device herein refers to a device hosted in a cloud environment and communicating with a control unit in the vehicle via a network. Cloud-based devices can take various forms, such as one or more servers connected via a network and used for processing and storing data.

[0046] A key feature of this embodiment is the ability of a cloud-based device to perform at least one step of the method. This device can, for example, perform tasks such as predicting communication quality, dynamically adapting to the timing of safety-related tasks, or processing the tasks themselves. The cloud-based device interacts with a control unit in the vehicle via a communication connection that ensures stable and, as far as possible, latency-free data transmission.

[0047] A key advantage of this cloud-based device is its ability to take over task processing and thus reduce the load on control units located within the vehicle. This enables optimized resource utilization and can contribute to improved reliability and security of the entire system. Furthermore, cloud-based devices can access more powerful computing resources, use more complex algorithms, and process large amounts of data, which is advantageous for performing demanding, safety-related tasks. Additionally, cloud-based devices can help improve communication stability, for example, by using redundant communication paths or fault-tolerant methods.

[0048] The aforementioned advantages also apply in a corresponding manner to a system, particularly a cloud-based system. This system has a control unit according to one of the previously described embodiments and external vehicle devices according to another of the previously described embodiments. The control unit and the external vehicle devices are interconnected via signaling technology and, in particular, spatially separated from each other.

[0049] Here, a cloud-based system is understood as a combination of a control unit integrated within the vehicle and one or more devices in the cloud. These components are interconnected via communication links that enable the exchange of data and control signals. Spatial separation between the vehicle control unit and the cloud-based devices is a key feature of this system. This allows for the utilization of cloud resources without burdening the vehicle's internal computing power and storage space.

[0050] A key advantage of this system lies in the optimized task allocation between the vehicle and the cloud. The vehicle control unit handles data acquisition and local control, while the cloud-based devices handle more complex data processing, data storage, and communication with external systems. This division of labor allows for leveraging the strengths of both components, resulting in optimized performance across the entire system. The signaling technology connecting the components ensures reliable information exchange.

[0051] By combining vehicle control units and cloud-based devices into a single system, the reliability, security, and efficiency of cloud-based systems in vehicles are further enhanced. Spatial separation of components allows the system to flexibly adapt to changing requirements and provides greater robustness compared to the failure of a single component. Optimized division of labor leads to improved resource utilization and faster response to critical situations.

[0052] The subject of this invention is also a computer program or computer program product comprising instructions that, when executed by a computer or a control unit or external device according to one of the foregoing embodiments, cause the computer / control unit / external device to perform at least one step of the method described in one of the foregoing embodiments. The computer program or computer program product may be stored on a machine-readable, particularly non-volatile, carrier or storage medium, such as semiconductor memory, hard disk memory, or optical memory.

[0053] The subject of this invention is also a computer-readable medium on which the aforementioned computer program is stored.

[0054] The term computer-readable medium can be understood as a physical medium that stores information in a form that can be read and processed by a computer or other electronic device. Such media can be, for example, CD-ROMs, DVDs, USB drives, hard drives, or other storage media that store digital data. This medium can include not only magnetic and optical storage media but also exist in various formats, such as as files, programs, or operating systems. This medium can also be used as part of a larger system or device, such as as part of a control unit in a computer or vehicle. In any case, a computer-readable medium enables the transfer of information between different electronic devices and the execution of programs or applications on those devices. Attached Figure Description

[0055] Embodiments of the invention are schematically illustrated in the accompanying drawings, and further elaborated in the following description. Elements shown in different figures that function similarly are referred to by the same reference numerals, wherein repeated descriptions of these elements are omitted.

[0056] Figure 1A schematic diagram illustrating a method, control unit, external vehicle device, system, computer program, and storage medium according to one embodiment is shown; and Figure 2 A schematic diagram illustrating the invention is shown according to one embodiment. Detailed Implementation

[0057] As previously described, this invention describes a method, control unit, external vehicle devices, systems, computer programs, and storage media that enable improved reliability and security of cloud-based systems in vehicles by dynamically adapting the execution time of safety-related tasks to predicted communication quality. This minimizes the risk of functional failures and safety incidents due to communication interference.

[0058] Figure 1 The diagram illustrates the flow of a method 100 for running a cloud-based system 30 in vehicle 1, and also shows the components involved and their interactions. System 30 monitors and controls various safety-related functions of vehicle 1. At the heart of system 30 is vehicle control unit 10, which is integrated into vehicle 1 and monitors and controls various sensors and actuators. Vehicle control unit 10 communicates with cloud-based device 40 via communication connection 5. Cloud-based device 40 is typically hosted in a cloud environment 8 and performs central processing and storage functions. Vehicle control unit 10 and cloud-based device 40 together constitute cloud-based system 30. In a first embodiment, computer program 20 stored on computer-readable medium 15 runs on vehicle control unit 10. In an alternative embodiment, computer program 40 stored on computer-readable medium 15 runs on cloud-based device 40. In another embodiment, it may be specified that computer program 20, or portions thereof, runs not only on vehicle control unit 10 but also on cloud-based device 40 and complements each other.

[0059] According to the embodiments described herein, method 100 begins with an optional step 101, namely, identifying the functions to be monitored. This step can be performed by the vehicle control unit 10 and / or the cloud-based device 40. These functions can be prioritized according to their security relevance. In step 102, the vehicle control unit 10 and / or the cloud-based device 40 collect the data required for prediction (e.g., signal strength, latency, historical data), which can be performed before, during, or after step 101. The data collected by the vehicle control unit 10 is transmitted to the cloud-based device 40. In a subsequent step 103, the cloud-based device 40 evaluates this data and creates a prediction of future communication quality. This prediction takes into account various factors, such as current signal strength, predicted network load, or historical data. The prediction method can vary considerably, for example, evaluating a QoS graph or applying a machine learning model.

[0060] Based on predictions, cloud-based device 40 calculates the optimal execution time for each safety-related task in subsequent step 104. This execution time is transmitted to vehicle control unit 10. Vehicle control unit 10 then accordingly controls the execution of the task in subsequent step 105. This dynamic adaptation of execution time is a core feature of method 100 and is used to ensure task execution despite potential communication interference. This adaptation can be performed in various ways, such as by advancing or postponing the execution time of at least one safety-related task.

[0061] Optionally, a maximum allowed execution time period (FTTI) can be defined for each task, within which the task must not only begin but also end. This FTTI takes into account safety requirements and the need for timely response. As described in the previously illustrated implementation, the FTTI can be adapted in a context-dependent manner (e.g., depending on weather, traffic conditions, or vehicle status).

[0062] Additionally, vehicle behavior can be optionally adapted (e.g., reducing speed, changing route) to improve communication quality. Adapting vehicle behavior supports dynamic adaptation and helps improve the reliability of the method.

[0063] Safety-related tasks are performed at dynamically adapted points in time. Depending on the task type and system prerequisites, these tasks can be executed in vehicle 1, in cloud environment 8, or in system 30, which comprises both. The results are transmitted to cloud-based device 40 for further processing.

[0064] Figure 2 The advantages of dynamically adapting execution time points are illustrated through three scenarios a, b, and c, where safety-related tasks are designed as diagnostic tests. In all scenarios, diagnostic tests are performed on safety-related vehicle components (e.g., braking systems, engine control units). This test requires communication 5 between vehicle 1 or vehicle control unit 10 and cloud-based device 40. Furthermore, connection quality 60 is shown digitally ("1" for good connection quality, or "0" for poor connection quality) over time 62, and the corresponding time points 71, 72, 73, and 74 of the diagnostic test are illustrated.

[0065] In scenario (a) above, a diagnostic process with fixed test intervals is shown. Tests are performed at regular intervals, regardless of current communication quality. If a connection interruption occurs during the diagnostic test (scenario b), for example due to poor connection quality, the diagnostic may fail to complete successfully, potentially leading to errors, security risks, or data loss, as indicated by Lightning 80. In scenario (b), specifically after waiting for a pre-given or pre-predictable time span, vehicle 1 is transitioned to a safe state.

[0066] In scenario (c) below, a diagnostic with dynamically adaptable test intervals is shown. Cloud-based device 40 predicts communication quality, such as... Figure 1 The test time points 71, 72, 73, and 74 are time-adapted so that the test is performed within a period of good connection quality ("1"). In this scenario, connection interruptions are avoided, leading to more reliable diagnostics and minimizing security risks. The graph shows that potential connection losses can be overcome by shifting the test interval.

Claims

1. A method (100) for operating a cloud-based system (30) in a vehicle (1), wherein the system (30) performs (105) a security-related task, the performance of which depends on communication (5) between the vehicle (1) and a cloud-based device (40), and wherein impairment of the communication quality between the vehicle (1) and the cloud-based device (40) is predicted (103), characterized in that, The execution time points (71, 72, 73, 74) of at least one of the security-related tasks are dynamically adapted (104) to address the predicted impairment of the communication quality.

2. The method (100) according to claim 1, characterized in that, The execution time points (71, 72, 73, 74) of the at least one security-related task are adapted by advancing the execution time points (71, 72, 73, 74).

3. The method (100) according to claim 1, characterized in that, The execution time points (71, 72, 73, 74) of the at least one security-related task are adapted by postponing the execution time points (71, 72, 73, 74).

4. The method (100) according to any one of the preceding claims, characterized in that, The safety-related task is the diagnostic testing of vehicle components.

5. The method (100) according to any one of the preceding claims, characterized in that, The impairment of communication quality is a connection interruption.

6. The method (100) according to any one of the preceding claims, characterized in that, The prediction of the impairment of the communication quality is based on the quality map (QoS map).

7. The method (100) according to any one of the preceding claims, characterized in that, The prediction of the impairment of the communication quality is based on a machine learning model.

8. The method (100) according to any one of the preceding claims, characterized in that, A maximum allowable execution time period (Fault Tolerance Time Period, FTTI) is set for the execution of the at least one security-related task, and the execution time points (71, 72, 73, 74) are dynamically adapted to start and / or end within the maximum allowable execution time period.

9. The method (100) according to claim 8, characterized in that, The maximum allowed execution time period is adapted in a context-dependent manner.

10. The method (100) according to any one of the preceding claims, characterized in that, The dynamic adaptation at the execution time point includes adapting at least one vehicle behavior to at least maintain or improve the availability of the communication (5) during the execution of the at least one safety-related task, and thus address the predicted impairment of the quality of the communication.

11. A control unit (10) for a vehicle (1), particularly a vehicle control unit, for operating a cloud-based system (30), characterized in that, The control unit (10) is configured to perform at least one step of the method (100) according to any one of claims 1 to 10.

12. An external, particularly cloud-based device (40) for running a cloud-based system (30) in a vehicle (1), the device communicating with a control unit (10) according to claim 11 and configured to perform at least one step of the method (100) according to any one of claims 1 to 10.

13. A system (30), particularly a cloud-based system, having a control unit (10) according to claim 11 and a vehicle-external device (40) according to claim 12, wherein the control unit (10) and the vehicle-external device (40) are interconnected by signal technology and are particularly spatially separated from each other.

14. A computer program (20) comprising instructions that, when executed by a computer or a control unit (10) according to claim 11 or a vehicle external device (40) according to claim 12, cause the computer / control unit / vehicle external device to perform at least one step of the method (100) according to any one of claims 1 to 10.

15. A computer-readable medium (15) having stored thereon the computer program (20) according to claim 14.

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