Method and device for detecting a hands-off state on a vehicle's steering wheel

Abstract

The invention relates to a method for detecting a hands-off state (6) on a steering wheel (51) of a vehicle (50), wherein at least one steering parameter (4) is detected on the steering wheel (51), wherein the detected at least one steering parameter (4) is supplied as input data to a trained machine learning model (5), wherein the machine learning model (5) is trained to detect a hands-off state (6) starting from at least the detected at least one steering parameter (4) and to output an associated state parameter as output data (20), wherein the trained machine learning model (5) is provided such that the trained machine learning model (5) has at least three component sections (5-x) that are successive with respect to a signal flow, wherein a trained component (7-1) is provided in a first component section (5-1).wherein at least one trained component (5-2-x) is provided in at least one middle component section (5-2) corresponding to a vehicle configuration of the vehicle (50), and wherein in a last component section (5-3) a trained component (7-3-x) corresponding to the vehicle configuration of the vehicle (50) is provided. The invention further relates to a device (1) for detecting a hands-off state (6) on a steering wheel (51).

Description

[0001] The invention relates to a method and a device for detecting a hands-off state on a steering wheel of a vehicle. Furthermore, the invention relates to a method and a device for providing a trained machine learning model for detecting a hands-off state on a steering wheel of a vehicle. Furthermore, the invention relates to a method and a device for maintaining a set of machine learning models provided in vehicles that have been trained to detect a hands-off state.

[0002] Sensors such as a capacitive steering wheel are used in vehicles to monitor driver activity. Such a steering wheel detects whether the driver has touched or not touched the steering wheel ("hands-off") using a capacitive sensor. The result is transmitted to the functions being used, such as a longitudinal and / or lateral guidance assistance system. The contact of the hands on the steering wheel can be used to determine driver activity and the driver's level of attention. For example, the system can indicate to the driver to place their hands on the steering wheel if it is detected that their hands have not been on the steering wheel for a specified period of time during lateral guidance.

[0003] To save additional costs for a capacitive sensor in the steering wheel, it is known to monitor driver activity using machine learning models, in particular artificial neural networks, based on a torque (hand torque) detected on the steering wheel. Such a method is known, for example, from DE 10 2019 211 016 A1.

[0004] A major challenge of steering torque-based detection is identifying the driver-induced steering torque in the measured (noisy) steering torque. Many factors can lead to noisy steering torque, in particular the position of the sensor (this is usually part of the steering gear or steering assistance, which, via the elasticity of the steering column in conjunction with the steering wheel, creates a system capable of torsional vibration, whose inherent dynamics make precise measurement of the driver-induced torque difficult); the strength of friction in the steering system; re-excitation from the road surface due to unevenness; the weight of the steering wheel / steering system; and vibration of the steering wheel due to an assistance function (e.g., due to haptic feedback when leaving the lane).

[0005] In addition, the characteristics (used for hands-off detection) of the measured steering torque can change due to external influences, e.g. temperature, vehicle load, the presence of a trailer, tire type and / or tire condition, steering system changes over the lifetime, road gradient / inclination / slant, etc.

[0006] For the most robust detection of the hands-off state, it is particularly important that all of the aforementioned influences and scenarios are present in the training data. However, this is difficult to achieve in practice, as the input data already spans a very large dimensional space.

[0007] The invention is based on the object of improving the recognition of a hands-off state using a trained machine learning model.

[0008] The object is achieved according to the invention by a method having the features of patent claim 1 and a device having the features of patent claim 3. Furthermore, the object is achieved according to the invention by a method having the features of patent claim 6 and a device having the features of patent claim 11. Furthermore, the object is achieved according to the invention by a method having the features of patent claim 12 and a device having the features of patent claim 13. Advantageous embodiments of the invention emerge from the subclaims.

[0009] In particular, according to a first aspect, a method for detecting a hands-off state on a steering wheel of a vehicle is provided, wherein at least one steering variable is detected on the steering wheel, wherein the detected at least one steering variable is supplied to a trained machine learning model as input data, wherein the machine learning model is trained to detect a hands-off state based on at least the detected at least one steering variable and to output an associated state variable as output data, wherein the trained machine learning model is provided such that the trained machine learning model has at least three component sections that follow one another with respect to a signal flow, wherein a trained component is provided in a first component section,wherein at least one trained component is provided in at least one middle component section corresponding to a vehicle configuration of the vehicle, and wherein in a last component section a trained component is provided corresponding to the vehicle configuration of the vehicle.

[0010] Furthermore, according to the first aspect, a device for detecting a hands-off state on a steering wheel of a vehicle is also provided, comprising at least one steering variable sensor configured to detect at least one steering variable on the steering wheel, and a data processing device, wherein the data processing device is configured to receive the detected at least one steering variable, provide a trained machine learning model, and supply the detected at least one steering variable to the trained machine learning model as input data, wherein the machine learning model is trained to detect a hands-off state based on at least the detected at least one steering variable and to output associated state information as output data, wherein the trained machine learning model is provided in such a way thatthat the trained machine learning model has at least three component sections that follow one another with respect to a signal flow, wherein the first component section provides a trained component, wherein at least one trained component is provided in at least one middle component section corresponding to a vehicle configuration of the vehicle, and wherein a trained component is provided in a last component section corresponding to the vehicle configuration of the vehicle.

[0011] Furthermore, in particular in a second aspect, a method is provided for providing a trained machine learning model for detecting a hands-off state on a steering wheel of a vehicle, wherein the trained machine learning model is provided for a plurality of vehicle configurations, wherein the trained machine learning model is provided such that the trained machine learning model has at least three component sections that follow one another with respect to a signal flow, wherein a trained component is provided in a first component section for all vehicle configurations, wherein at least one trained component is provided in at least one middle component section for at least a subset of all vehicle configurations, and wherein in a last component section a trained component is provided exclusively for a vehicle configuration corresponding thereto,and wherein, to provide the trained machine learning model for a given vehicle configuration, the respective trained components are selected and provided taking into account the vehicle configuration.

[0012] According to the second aspect, in particular, a device is also provided for providing a trained machine learning model for detecting a hands-off state on a steering wheel of a vehicle, comprising a central server, wherein the central server is configured to provide the trained machine learning model for a plurality of vehicle configurations, and to provide the trained machine learning model in such a way that the trained machine learning model has at least three component sections that follow one another with respect to a signal flow, wherein a trained component is provided in a first component section for all vehicle configurations, wherein at least one trained component is provided in at least one middle component section for at least a subset of all vehicle configurations,and wherein, in a final component section, a trained component is provided exclusively for a vehicle configuration corresponding thereto, and wherein, to provide the trained machine learning model for a given vehicle configuration, the respective trained components are selected and provided taking the vehicle configuration into account.

[0013] According to a third aspect, in particular, a method is provided for maintaining an entirety of machine learning models provided in vehicles and trained to recognize a hands-off state, wherein recorded and / or collected maintenance data relating to respective components of at least three component sections of the respective trained machine learning model are received from the vehicles, wherein the received maintenance data are clustered according to components, and wherein the machine learning model is retrained according to the clustered components, taking into account the received maintenance data, and the respective retrained machine learning model is provided.

[0014] According to the third aspect, in particular, a device for servicing a totality of machine learning models provided in vehicles and trained to recognize a hands-off state is also created, comprising a central server, wherein the central server is configured to receive maintenance data recorded and / or collected by the vehicles relating to respective components of at least three component sections of the respective trained machine learning model from the vehicles, to cluster the received maintenance data according to components, and to retrain the machine learning model taking into account the received maintenance data in each case according to the clustered components and to provide the respectively retrained machine learning model.

[0015] The methods and devices according to the individual aspects make it possible to provide a machine learning model that is generalizable across vehicle variants or configurations, while simultaneously providing efficient and traceable lifecycle management for detecting the hands-off state. Characteristics from individual vehicle configurations can play an important role in detecting the hands-off state. Using the proposed methods and devices, these characteristics can be individually considered. In principle, they could also be mapped into a large machine learning model with a large amount of data. A disadvantage of this, however, is that a large amount of training data is required, which subsequently makes it impossible to clearly differentiate between individual vehicle configurations. If any complaints arise during application, the entire machine learning model must be retrained.This is computationally and resource-intensive and, in particular, requires complete validation before it can be deployed in the vehicle. While different, smaller machine learning models are trained for each vehicle configuration, they can be specifically adapted, versioning and managing the machine learning models is more complex than with a global machine learning model. Furthermore, shared characteristics that are similar for every vehicle configuration are not efficiently considered in such an approach.

[0016] It is a basic idea of ​​the invention to provide the trained machine learning model such that the trained machine learning model has at least three component sections that follow one another with respect to a signal flow. In a first component section, a (single) trained component is provided. This trained component is used by all vehicles or all vehicle configurations. In at least one middle component section, at least one trained component corresponding to a vehicle configuration of the vehicle is provided. These components can be shared by multiple vehicles or vehicle configurations. In a last component section, a (single) trained component corresponding to the vehicle configuration of the vehicle is provided. This trained component is used by only one vehicle configuration.In particular, the invention makes it possible to provide different variants of the machine learning model while still structurally exploiting characteristics shared by multiple vehicles or vehicle configurations when detecting the hands-off state. Furthermore, the proposed structure of the trained machine learning model makes it possible to simplify maintenance. In particular, only the component or components that exhibit a problem and / or a complaint when detecting the hands-off state need to be maintained, in particular retrained. Unaffected components do not need to be maintained, in particular retrained. This is especially true when a component is used in multiple vehicle configurations.This can be particularly true for the trained component of the first component section, since this is used by all vehicle configurations, and for one or more components of the at least one middle component section, which can be used by multiple vehicle configurations. If problems and / or complaints arise with multiple vehicle configurations that all include this component or these components, this component or these components will be retrained, while the other components will not be affected by the retraining.

[0017] A steering variable is, in particular, a variable that represents and / or describes a current state of the steering wheel. A steering variable is, in particular, a torque, which is detected in particular by means of a torque sensor on the steering wheel. In principle, however, a steering variable can also be another variable detected directly or indirectly on the steering wheel. For example, it can be provided to detect a current at an electrical machine on the steering wheel and use it as a steering variable. The hands-off state can be detected exclusively on the basis of the steering variable detected on the steering wheel, in particular a detected torque. However, it is also possible, in particular, for the machine learning model to be provided with further (steering) variables that are detected on the steering wheel (e.g., a steering wheel angle and / or a steering wheel angular velocity, etc.).) and the trained machine learning model recognizes the hands-off state taking this additional variable(s) into account. Variables that are not recorded at the steering wheel, such as vehicle speed, lateral acceleration, yaw rate, wheel ticks, damper information and / or other driving dynamics variables, as well as weather and / or situation data, etc., can be considered as context information and also fed to the trained machine learning model as input data. In particular, however, no capacitive sensor is provided on the steering wheel.

[0018] A vehicle configuration is, in particular, a description of the properties of a vehicle. These properties include, in particular, a vehicle class, a vehicle model, and / or individual vehicle equipment. The vehicle configuration is used to select, in particular, the components of the at least one middle component section and the last component section that are relevant for detecting the hands-off state.

[0019] The first component section comprises, in particular, a (single) trained component that is used by all vehicles or all vehicle configurations. In particular, all vehicles or vehicle configurations use the same weights and / or parameters of this one component.

[0020] For a given vehicle configuration, the at least one central component section includes at least one trained component corresponding to the vehicle configuration. For example, individual components can be provided for the following areas: e.g., in the drive area: electric drive, combustion engine, or hybrid; in the vehicle class area: compact class, sports utility vehicle (SUV), minibus, etc.

[0021] For a given vehicle configuration, the last component section contains a trained component that corresponds to the respective vehicle configuration. For example, it may be possible to provide an individual component for each vehicle model, e.g., vehicle model 1, vehicle model 2, vehicle model 3, etc. In particular, exactly one component is then active for each vehicle model in the last component section.

[0022] Components are, in particular, components defined virtually in memory. A component is, in particular, a definable area of ​​the machine learning model's signal processing with respect to a signal flow. In principle, however, it is also possible to implement the components in physical form.

[0023] It may be provided that a hands-on state is also detected when the hands-off state is detected.

[0024] A hands-off state is, in particular, a state in which the driver does not touch the steering wheel. In particular, none of the driver's fingers are in contact with the steering wheel. Detecting the hands-off state can, in particular, comprise providing a hands-off state signal. This comprises, for example, a hands-off probability or coded signals for the states "hands-off detected" and "hands-off not detected." A hands-on state is, in particular, a state in which the driver touches the steering wheel. A hands-on / hands-off state can also be provided, for example as a hands-on / hands-off state signal with, in particular, at least two signal states (e.g., "hands-on detected" or "hands-off detected").

[0025] The machine learning model and in particular the components may comprise one or more neural networks. The neural network(s) may comprise several inner layers. The machine learning model comprises one or more artificial recurrent neural networks that process the input data X at each time point t. t process and a hands-off probability y t in [0,1] outputs: y t = p(x t | x 0:t-1 ). In particular, a recurrent neural network has a so-called memory h, in which information from previous time steps is stored and which can be used for the output at the current time step.

[0026] The output data of the trained machine learning model is further processed, for example, by filtering before being processed by subsequent functions (e.g., a lateral guidance assistant). In particular, it can be provided that, based on a comparison of the hands-off probability with a predefined threshold, a binary hands-off signal is provided (with the two states "hands-off detected" and "hands-off not detected").

[0027] During a training phase, the machine learning model is trained in particular for different vehicle configurations. This is done using training data that comprise pairs in which data of the at least one steering variable, in particular torque data, are each paired with a hands-off state (as ground truth). The data of the at least one steering variable, in particular the torque data, are in particular time series of the at least one steering variable recorded at the steering wheel, in particular time series of torques recorded at the steering wheel. The training data is obtained in particular with the aid of test drives and / or in simulators for the individual vehicle configurations. In principle, the provision of training data can be carried out in particular according to the method described in DE 10 2019 211 016 A1. Depending on the vehicle configuration, it is provided that during training orDuring a training step, only those components are changed that correspond to the vehicle configuration to which the training data belongs. In other words, the component of the first component section is always trained, at least during the initial training. Components of the at least one middle component section and the last component section, however, are only changed during training if they belong to the vehicle configuration that corresponds to the current training data. Training is otherwise carried out in a conventional manner, in particular by means of supervised learning. In particular, during training, the parameters of the machine learning model (in particular the weights) are adjusted by means of back projection until a calculated error of the output compared to the ground truth is below a predetermined threshold.

[0028] Maintenance data can, for example, include information on customer complaints, such as information on frequently occurring false warnings. The false warnings can be identified by vehicle users, for example, and transmitted to the central server as maintenance data. The false warnings are specifically assigned to or linked to the respective vehicle configurations, allowing them to be clustered by components and the machine learning model to be retrained according to the clustered components. Furthermore, maintenance data can also be generated by recording fleet data and performing a statistical analysis. For example, the confidence of the outputs of the trained machine learning model can be evaluated as part of the statistical analysis.For example, the confidence can be compared with a predefined threshold, whereby maintenance data, especially for the respective vehicle configuration, is generated and transmitted if the confidence of an output is below the predefined threshold. Based on the respective vehicle configuration, the maintenance data can be clustered by component.

[0029] Parts of the devices, in particular the data processing device and the central server, can be implemented individually or collectively as a combination of hardware and software, for example as program code executed on a microcontroller or microprocessor. However, it can also be provided that parts are implemented individually or collectively as an application-specific integrated circuit (ASIC) and / or a field-programmable gate array (FPGA). The data processing device and the central server each comprise, in particular, at least one computing device and at least one memory. Furthermore, the data processing device and the central server can each have at least one communication interface for communication.

[0030] In particular, it can be provided that the methods according to the second aspect and the third aspect are each carried out or designed as computer-implemented methods.

[0031] In one embodiment, the vehicle configuration of the last component section relates to a vehicle model. This allows a trained component to be provided for each vehicle model. Characteristics for recognizing the hands-off state can then be individually considered for each vehicle model and learned during training.

[0032] In one embodiment, the provision includes loading the trained machine learning model into a memory of a control unit of the respective vehicle. The provided trained machine learning model can then be used in the vehicle to detect the hands-off state.

[0033] In one embodiment, it is provided that trained components of the trained machine learning model that do not correspond to the vehicle configuration of the respective vehicle are deactivated or will be deactivated. This can save memory and / or computing resources when applying the trained machine learning model, since only the necessary components are used. The deactivation can be carried out in particular in such a way that memory areas containing the unused components are released. Furthermore, it can be provided that when loading the trained machine learning model (or a description and parameters thereof) into a memory of a data processing device and / or a device (e.g. control unit) of a vehicle, only the non-deactivated components are loaded into the memory, but not the deactivated components.

[0034] In one embodiment, it is provided that, in order to provide the trained machine learning model, information relating to the vehicle configuration is recorded and / or obtained, wherein the trained machine learning model is provided based on the recorded and / or obtained information. This allows the trained machine learning model to be individually configured for a vehicle. This can take place, for example, at a suitable point during the manufacture of the vehicle or of a device provided for the vehicle (e.g., control unit) which is intended to detect the hands-off state. The information can be, for example, a serial number, a unique identification number and / or a barcode and / or a QR code. Based on this, the required components of the trained machine learning model can be determined.The trained machine learning model is then deployed by providing the specific components, linked to each other using signal technology.

[0035] In one embodiment, for a vehicle configuration that is not yet known, at least one component for the at least one middle component section and / or one component for the last component section is generated, trained, and provided. This allows a trained machine learning model to be provided for a vehicle configuration that is not yet known, based on the remaining components that have already been trained.

[0036] Further features of the respective devices are described in the embodiments of the respective methods. The advantages of the respective devices are the same as those of the embodiments of the respective methods.

[0037] Further features regarding the design of the respective aspects are derived from the description of the designs of the other aspects. The advantages of the respective aspects are the same as those of the other aspects.

[0038] Furthermore, a steering system is also provided, comprising a device for detecting a hands-off state on a steering wheel of a vehicle according to one of the described embodiments.

[0039] Furthermore, a vehicle is also provided, comprising a steering system according to one of the described embodiments and / or a device for detecting a hands-off state on a steering wheel of a vehicle according to one of the described embodiments.

[0040] The invention will be explained in more detail below using preferred embodiments with reference to the figures. Fig. 1 is a schematic diagram illustrating embodiments of the device for detecting a hands-off state on a steering wheel; Fig. 2 a schematic representation to illustrate a structure of the trained machine learning model; Fig. 3 shows a schematic flow diagram to illustrate embodiments of the method for providing a trained machine learning model for detecting a hands-off state on a steering wheel of a vehicle; Fig. 4 is a schematic flow diagram illustrating embodiments of the method for maintaining a set of machine learning models provided in vehicles and trained to detect a hands-off state; Fig. 5a-5c schematic representations of the machine learning model to illustrate the retraining in different constellations.

[0041] The Fig. 1 shows a schematic representation of an embodiment of the device 1 for detecting a hands-off state 6 on a steering wheel 51. The device 1 is arranged in particular in a vehicle 50 and is part of a steering system 60 there. The method described in this disclosure according to the first aspect is illustrated and explained in more detail below using the device 1.

[0042] The device 1 comprises a steering variable sensor 2 and a data processing device 3. The steering variable sensor 2 is configured to detect a steering variable 4 at the steering wheel 51 of the vehicle 50. The steering variable sensor 2 is, for example, a torque sensor, and the steering variable 4 is a torque. In principle, additional steering variable sensors can be provided, alternatively or additionally, to detect additional steering variables. The device 1 can comprise a communication interface (not shown) for communication.

[0043] The data processing device 3 comprises a computing device 3-1 and a memory 3-2. The computing device 3-1 is configured to perform the computing operations necessary for implementing measures of the method and can access data stored in the memory 3-2 for this purpose.

[0044] The data processing device 3 is configured to receive the recorded at least one steering variable 4, a trained machine learning model 5 (cf. Fig. 2) and to feed the recorded at least one steering variable 4 to the trained machine learning model 5 as input data.

[0045] The machine learning model 5 ( Fig. 2) is trained to recognize the hands-off state 6 based on at least the recorded at least one steering variable 4 and to use associated state information as output data 20 ( Fig. 2). The trained machine learning model 5 is provided such that the trained machine learning model 5 has at least three component sections 5-1, 5-2, 5-3 that follow one another with respect to a signal flow. The first component section 5-1 provides a trained component 7-1. At least one middle component section 5-2 provides at least one trained component 7-2-x corresponding to a vehicle configuration of the vehicle 50. A last component section 5-3 provides a trained component 7-3-x corresponding to the vehicle configuration of the vehicle 50.

[0046] The hands-off state 6 is fed, for example, as a state signal or state information to a control unit 52 of the vehicle 50 for further processing. The control unit 52 can, for example, be a lateral guidance assistant or another assistance system. The state signal or state information can, for example, contain a hands-off probability or a binary state value with the two states "hands-off detected" and "hands-off not detected."

[0047] The Fig. 2 shows a schematic representation to illustrate a structure of the trained machine learning model 5. The component sections 5-1, 5-2, 5-3 are shown. More than just one middle component section 5-2 can also be provided. The at least one recorded steering variable 4 is supplied to the trained machine learning model 5 as input data. The signal flow runs from the first component section 5-1 via the middle component section 5-2 to the last component section 5-3. In this case, the (only) component 7-1 of the first component section 5-1 is always active (i.e., in all vehicle configurations). The components 7-2-x, 7-3-x of the middle component section 7-2 and the last component section 7-3, on the other hand, are based on an existing vehicle configuration of the vehicle 50 ( Fig. 1), in which the trained machine learning model 5 is used, is selected and used. In the last component section 5-3, only one of the components 7-3-x is active when applied and outputs the hands-off state 6.

[0048] The components 7-2-x of the middle component section 5-2 can, for example, correspond to the following properties of the vehicle: Component 7-2-1: electric drive Component 7-2-2: Internal Combustion Engine Component 7-2-3: Compact class Component 7-2-4: Sports Utility Vehicle (SUV) Component 7-2-5: Minibus etc.

[0049] The components 7-3-x of the last component section 5-3 can, for example, correspond to the following properties of the vehicle: Component 7-3-1: Vehicle Model 1 Component 7-3-2: Vehicle Model 2 Component 7-3-3: Vehicle Model 3 Component 7-3-4: Vehicle Model 4etc.

[0050] For example, if a vehicle configuration includes the vehicle of vehicle model 4 having an electric drive and being a minibus, components 7-1, 7-2-1, 7-2-5, and 7-3-4 are active. In particular, a signal flow only occurs between these active components 7-1, 7-2-1, 7-2-5, and 7-3-2. Component 7-3-2 outputs the hands-off state 6 as output data 20.

[0051] The remaining components 7-2-x and 7-3-x are not used and can be deactivated, in particular to save memory and computing resources.

[0052] The Fig. Figure 3 shows a schematic flow diagram to illustrate embodiments of the method for providing a trained machine learning model for detecting a hands-off state on a steering wheel of a vehicle. The method is implemented by means of a device 40 ( Fig. 1) according to the second aspect. The device 40 comprises a central server 41. The central server 41 comprises a computing device 41-1 and a memory 41-2. The computing device 41-1 is configured to perform the computing operations necessary for implementing measures of the method and can access data stored in the memory 41-2 for this purpose. The central server 41 can comprise a communication interface (not shown) for communication.

[0053] In a measure 100, the trained machine learning model is provided for a plurality of vehicle configurations, wherein the trained machine learning model is provided such that the trained machine learning model has at least three component sections that follow one another with respect to a signal flow, wherein a trained component is provided in a first component section for all vehicle configurations, wherein at least one trained component is provided in at least one middle component section for at least a subset of all vehicle configurations, and wherein in a last component section a trained component is provided exclusively for a vehicle configuration corresponding thereto.

[0054] In a measure 101, in order to provide the trained machine learning model for a given vehicle configuration, the respective trained components are selected and provided taking the vehicle configuration into account. According to the example already used above, the given vehicle configuration may provide that the vehicle of the vehicle model 4 has an electric drive and is a minibus. Therefore, the following trained components 7-1, 7-2-1, 7-2-5 and 7-3-4 ( Fig. 2) in measure 101 and the trained machine learning model configured in this way is provided, in particular in the form of a structural description which describes the trained components 7-1, 7-2-1, 7-2-5 and 7-3-4 and the respective links between the trained components 7-1, 7-2-1, 7-2-5 and 7-3-4 as well as the respective parameters (weights, hyperparameters, etc.).

[0055] In a measure 102, it can be provided that the provision includes loading the trained machine learning model into a memory of a device (e.g., control unit) of the respective vehicle. This can be done, for example, during the manufacture of the vehicle and / or the device.

[0056] Measure 101 may provide that trained components 7-2-x, 7-3-x ( Fig. 2) of the trained machine learning model that do not correspond to the vehicle configuration of the respective vehicle are deactivated. The structural description then no longer contains any information about these trained components 7-2-x, 7-3-x.

[0057] It can be provided in a measure 99 that in order to provide the trained machine learning model 5, information 42 ( Fig. 1) relating to the vehicle configuration is recorded and / or obtained, wherein the trained machine learning model 5 is provided based on the recorded and / or obtained information 42. The information 42 contains, in particular, details of the properties of the respective vehicle 50 corresponding to the individual components 5-2-x, 5-3-x. Based on the information 42, the central server 40 can, for example, look up in a database which components 5-2-x, 5-3-x must be used or activated for the trained machine learning model 5.

[0058] It can be provided that, in the case of a vehicle configuration that is not yet known, at least one component 5-2-x for the at least one middle component section 5-2 and / or a component 5-3-x for the last component section 5-3 is generated, trained, and provided. This is also done via the central server 40. For this purpose, the central server 40 receives, in particular, training data corresponding to the vehicle configuration that is not yet known in order to be able to train the newly generated component 5-2-x, 5-3-x. The training takes place in the same way as already explained in the general description.

[0059] The Fig. Figure 4 shows a schematic flow diagram to illustrate embodiments of the method for maintaining a set of machine learning models provided in vehicles and trained to detect a hands-off state. The method is implemented in particular by means of a central server 40 ( Fig. 1) is carried out.

[0060] In a measure 200, recorded and / or collected maintenance data 43 ( Fig. 1) concerning respective components 7-1, 7-2-x, 7-3-x of the at least three component sections 5-1, 5-2, 5-3 of the respective trained machine learning model 5 are received from the vehicles 50 via the central server 40. It is assumed here that a plurality of vehicles 50 are present in the same or different vehicle configurations. These vehicles 50 collect and transmit the maintenance data 43.

[0061] In a measure 201, the received maintenance data 43 are sorted according to components 5-1, 5-2-x, 5-3-x ( Fig. 2) clustered (i.e., specifically summarized or grouped).

[0062] In a measure 202, the machine learning model is retrained, taking into account the received maintenance data 43, according to the clustered components 5-1, 5-2-x, 5-3-x, respectively, and the retrained machine learning model 5 is made available. The retraining only affects those components 5-1, 5-2-x, 5-3-x that have been identified as problematic and / or for which a complaint has been raised by a vehicle user. Using the complaints, which may, for example, include that the recognition of the hands-off state does not function reliably, those components 7-1, 7-2-x, 7-3-x that are affected by the complaint can be identified, in particular through clustering or the formation of intersections.

[0063] In particular, it is provided in a measure 203 that the machine learning model 5 ( Fig. 1) is transmitted to the respective vehicle 50 and activated there, for example by loading the retrained machine learning model 5 into the memory of a device (e.g. a control unit).

[0064] The Fig. 5a, Fig. 5b and Fig. 5c shows schematic representations of the machine learning model 5 to illustrate the retraining in different constellations.

[0065] In the Fig. 5a, for example, only one complaint can be derived from the maintenance data for component 7-3-1 (e.g., "Vehicle Model 1") of the last component section 5-3. Accordingly, only this component 7-3-1 is retrained with training data. In this case, the training data corresponds to an associated vehicle configuration. The parameters (weights and hyperparameters, etc.) of the other components are not changed during retraining. The remaining active components of an associated vehicle configuration are used only for inference.

[0066] In the Fig. 5b, for example, from the maintenance data clustered by components, a complaint can be derived for components 7-2-1 and 7-2-4 (e.g., "electric drive" and "SUV") of the middle component section 5-2 and for components 7-3-2 (e.g., "vehicle model 2") and 7-3-3 (e.g., "vehicle model 3"). Accordingly, only these components 7-2-1, 7-2-4, 7-3-2, and 7-3-3 are retrained with training data. In this case, the training data corresponds to the associated vehicle configurations. The parameters (weights and hyperparameters, etc.) of the other components are not changed during retraining. The remaining active components of an associated vehicle configuration are used only for inference.

[0067] In the Fig.For example, in Figure 5c, a complaint can be derived from the maintenance data clustered by components, affecting all vehicle configurations. Accordingly, all components 7-1, 7-2-x, 7-3-x of all component sections 5-1, 5-2, 5-3 are then retrained with training data. In this case, the training data corresponds to all vehicle configurations.

[0068] After retraining, optimization, testing, and release of the retrained machine learning model 5 are planned. The retrained machine learning model 5 is then transmitted to the respective vehicles 50 and applied there. List of reference symbols 1 device 2 steering size sensor 3 Data processing facility 3-1 Calculation device 3-2 Memory 4 Steering size 5 trained machine learning model 5-1 first component section 5-2 middle component section 5-3 last component section 6 Hands-off state 7-1 trained component 7-2-x trained component 7-3-x trained component 20 initial data 40 Device 41 central server 41-1 Computing device 41-2 Storage 42 Information 43 Maintenance data 50 vehicles 51 Steering wheel 60 Steering system 99-102 Measures of the procedure 200-202 Measures of the procedure

Claims

[1] Method for detecting a hands-off state (6) on a steering wheel (51) of a vehicle (50), wherein at least one steering variable (4) is detected on the steering wheel (51), wherein the detected at least one steering variable (4) is fed to a trained machine learning model (5) as input data, wherein the machine learning model (5) is trained to recognize a hands-off state (6) based on at least the detected at least one steering variable (4) and to output an associated state variable as output data (20), wherein the trained machine learning model (5) is provided such that the trained machine learning model (5) has at least three component sections (5-x) following one another with respect to a signal flow, wherein a trained component (7-1) is provided in a first component section (5-1), wherein at least one trained component (5-2-x) is provided in at least one middle component section (5-2) corresponding to a vehicle configuration of the vehicle (50), and wherein in a last component section (5-3) a trained component (7-3-x) corresponding to the vehicle configuration of the vehicle (50) is provided. [2] Method according to claim 1, characterized by that the vehicle configuration of the last component section (5-3) concerns a vehicle model. [3] Device (1) for detecting a hands-off state (6) on a steering wheel (51) of a vehicle (50), comprising: at least one steering variable sensor (2) which is designed to detect at least one steering variable (4) on the steering wheel (51), and a data processing device (3), wherein the data processing device (3) is configured to receive the detected at least one steering variable (4), to provide a trained machine learning model (5) and to supply the detected at least one steering variable (4) to the trained machine learning model (5) as input data, wherein the machine learning model (5) is trained to recognize a hands-off state (6) based on at least the detected at least one steering variable (4) and to output associated state information as output data (20), wherein the trained machine learning model (5) is provided such that the trained machine learning model (5) has at least three component sections (5-x) following one another with respect to a signal flow, wherein the first component section (5-1) provides a trained component (7-1), wherein at least one trained component (7-2-x) is provided in at least one middle component section (7-2) corresponding to a vehicle configuration of the vehicle (50), and wherein in a last component section (5-3) a trained component (7-3-x) corresponding to the vehicle configuration of the vehicle (50) is provided. [4] Steering system (60) comprising a device (1) according to claim 3. [5] Vehicle (50) comprising a device (1) according to claim 3 and / or a steering system (60) according to claim 4. [6] Method for providing a trained machine learning model (5) for detecting a hands-off state (6) on a steering wheel (51) of a vehicle (50), wherein the trained machine learning model (5) is provided for several vehicle configurations, wherein the trained machine learning model (5) is provided such that the trained machine learning model (5) has at least three component sections (5-x) following one another with respect to a signal flow, wherein a trained component (7-1) is provided in a first component section (5-1) for all vehicle configurations, wherein at least one trained component (7-2-x) is provided in at least one middle component section (5-2) for at least a subset of all vehicle configurations, and wherein in a last component section (5-3) a trained component (7-3-x) is provided exclusively for a vehicle configuration corresponding thereto, and wherein, to provide the trained machine learning model (5) for a given vehicle configuration, the respective trained components (7-1,7-2-x,7-3-x) are selected and provided taking into account the vehicle configuration. [7] Method according to claim 6, characterized by that the provision comprises loading the trained machine learning model (5) into a memory (3-2) of a control unit of the respective vehicle. [8] Method according to claim 6 or 7, characterized by that trained components (7-2-x,7-3-x) of the trained machine learning model (5) that do not correspond to the vehicle configuration of the respective vehicle (50) are deactivated. [9] Method according to one of claims 6 to 8, characterized byin that, in order to provide the trained machine learning model (5), information (42) relating to the vehicle configuration is detected and / or obtained, wherein the trained machine learning model (5) is provided on the basis of the detected and / or obtained information (42). [10] Method according to one of claims 6 to 9, characterized by that, in the case of a vehicle configuration that is not yet known, at least one component (7-2-x) for the at least one middle component section (5-2) and / or one component (7-3-x) for the last component section (5-3) is generated, trained and provided. [11] Device (40) for providing a trained machine learning model (5) for detecting a hands-off state (6) on a steering wheel (51) of a vehicle (50), comprising: a central server (41), wherein the central server (41) is configured to provide the trained machine learning model (5) for a plurality of vehicle configurations, and to provide the trained machine learning model (5) in such a way that the trained machine learning model (5) has at least three component sections (5-x) following one another with respect to a signal flow, wherein a trained component (7-1) is provided in a first component section (5-1) for all vehicle configurations, wherein at least one trained component (7-2-x) is provided in at least one middle component section (5-2) for at least a subset of all vehicle configurations, and wherein in a last component section (5-3) a trained component (7-3-x) is provided exclusively for a vehicle configuration corresponding thereto, and wherein, to provide the trained machine learning model (5) for a given vehicle configuration, the respective trained components (7-1,7-2-x,7-3-x) are selected and provided taking into account the vehicle configuration. [12] Method for maintaining a set of machine learning models (5) provided in vehicles (50) and trained to detect a hands-off state (6), wherein recorded and / or collected maintenance data (43) relating to respective components (7-1, 7-2-x, 7-3-x) of at least three component sections (5-x) of the respective trained machine learning model (5) are received by the vehicles (50), wherein the received maintenance data (43) are clustered according to components (7-1,7-2-x,7-3-x), and wherein the machine learning model (5) is retrained taking into account the received maintenance data (43) in each case according to the clustered components (7-1,7-2-x,7-3-x) and the respectively retrained machine learning model (5) is provided. [13] Device (40) for maintaining a set of machine learning models (5) provided in vehicles (50) and trained to detect a hands-off state (6), comprising: a central server (41), wherein the central server (41) is configured to receive maintenance data (43) recorded and / or collected by the vehicles (50) relating to respective components (7-1, 7-2-x, 7-3-x) of at least three component sections (5-x) of the respective trained machine learning model (5) from the vehicles (50), to cluster the received maintenance data (43) according to components (7-1, 7-2-x, 7-3-x), and to retrain the machine learning model (5) taking into account the received maintenance data (43) in each case according to the clustered components (7-1, 7-2-x, 7-3-x) and to provide the respectively retrained machine learning model (5).

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