DE102022106092A1 - Method, device and computer program for supporting an occupant of a vehicle in operating a vehicle system, in particular an information system, communication system and display operating system of a vehicle - Google Patents

Abstract

Method, device and computer program for supporting an occupant of a vehicle (10, 11, 12, 20, 21, 22) when operating the vehicle, in particular a vehicle system, in particular an information system, communication system or a display operating system of a vehicle (10, 11, 12, 20, 21, 22). The method (100) comprises: - Determining (101) an occupant error during one or more first operating processes of a first vehicle (10, 11, 12); - Detecting (102) a second operating process of a second vehicle (20, 21, 22) ; wherein the second operation has at least some degree of similarity to the one or more first operations; and then: - Executing (103) a measure in relation to a possible occupant error in the second operation.

Description

The invention relates to a method, device and a computer program for supporting an occupant of a vehicle in operating a vehicle system, in particular an information system and/or communication system, and/or the display operating system of a vehicle. The invention further relates to a corresponding system consisting of several, in particular a large number, of vehicles.

Occupants (e.g. drivers or passengers) of a vehicle often tend to make certain, sometimes person-specific errors that occur particularly in certain contexts under certain conditions. Some errors may therefore be repeated several times or frequently. Most of the time, these cannot be derived or predicted through analytical connections because the underlying perceptual, cognitive and psychological connections can be complex.

The present document deals with the technical task of supporting an occupant of a vehicle in operating a vehicle system, in particular an information system and/or communication system, and/or the display operating system of a vehicle. In particular, the technical task includes a technical solution with which an improved operation of a vehicle system, in particular an information system and/or communication system, and/or the display operating system of a vehicle is made possible or the occupant of the vehicle is supported in doing so.

The task is solved by each of the independent claims. Advantageous embodiments are described, among other things, in the dependent claims. It should be noted that additional features of a patent claim dependent on an independent patent claim can form a separate invention independent of the combination of all the features of the independent patent claim, without the features of the independent patent claim or only in combination with a subset of the features of the independent patent claim can be made the subject of an independent claim, a division application or a subsequent application. This applies equally to technical teachings described in the description, which may constitute an invention independent of the features of the independent patent claims.

According to a first aspect, a method for supporting an occupant of a vehicle when operating a vehicle system, in particular an information system and/or communication system, and/or the display operating system of a vehicle is proposed. The method includes: determining an occupant error during one or more first operations of a first vehicle; recognizing or predicting a second operation of a second vehicle; wherein the second operation has at least some degree of similarity to the one or more first operations; and then: taking action regarding a possible occupant error during the second operation.

The term “operating process” refers in particular to an operation that deviates from the (classic) driving activity, in particular an operation of the vehicle, in particular of a vehicle system (e.g. an information system, communication system,) that is superimposed on the driving activity or is carried out at least partially simultaneously with the driving activity. as well as the display and operating system of a vehicle).

The term “operating error” by an occupant of the (respective) vehicle is, in particular, an incorrect information input into a vehicle system that does not lead to the desired result, an information output and/or action or interaction with a display operating system (in particular also to be understood as the human-machine interface) of a vehicle.

In particular, the one or more operating errors are those that are caused by the occupant of the vehicle, in particular by perceptual, cognitive and/or psychological effects.

Determining an occupant error during one or more first operating processes of a first vehicle can include determining various, for example qualitative and/or quantitative, parameters of the occupant error. These can be taken into account in the process (e.g. when selecting the measure, determining or adjusting the parameters of the measure, etc.). For example, if the occupant made a typing error when entering information or operated a different control element instead of or next to the desired one, this can be determined and taken into account as a parameter of the occupant error.

In particular, the operating process relates to a system or functionality of the vehicle that includes information output (for perception by the occupant), information input (e.g. offer to enter the information, e.g. On tap, selection of options, by the occupant). For example, the corresponding vehicle system can be a navigation system, an infotainment system or a communication system of the vehicle. Such processes are also subsumed under the term “operation of the vehicle”.

In the context of this document, the term “recognizing” is also understood to mean, in particular, predication. Determination can in particular mean that a (e.g. second) operating procedure is predicted in relation to the future (e.g. with a certain degree of probability and/or for a certain time interval). Such recognition or prediction can relate to an operating action that has only been partially implemented (e.g. only to a small extent) or has not yet been started. A measure can be carried out before or at an early stage of executing the operating process (and can therefore be particularly effective).

The determination, in particular prediction, can be based on, for example, a determined intention of the occupant to carry out a (certain) operating procedure and/or various other signs, e.g. parameter patterns that often occur shortly before a (certain) operating procedure or an operating procedure (of a certain type).

For example, only or primarily the first operating processes that have been recognized, in particular predicted, with at least a certain degree of probability are taken into account.

The (second) occupant error can be an occupant error that has not yet been recognized or is recognizable and/or is taking place or will possibly take place in a second operating process that has not yet taken place (but, for example, has only been predicted) or is not sufficiently advanced.

The second operation may differ from the first operation. For example, it can be a related operation and/or one that is at least 30, 50, 70% the same (but not exactly the same) operation. For example, the determined or expected second operating process can be similar or the same as the one or more first operating processes with regard to at least 30, 50, 70% of the features. This increases the number of second operating procedures in which the occupants can be supported as part of the procedure.

For example, by taking the similarity into account, it can be achieved that no or no inappropriate (e.g. counterproductive or error-causing) measure is carried out due to a different, dissimilar first operating process.

Executing the at least one measure can include or be a selection of one (of several possible) measures and/or determining or adjusting the parameters of the measure and/or initiating the measure. A measure can include or be a (e.g. appropriate) change in the operating mode of a vehicle system, in particular an information system, communication system and display operating system of a vehicle.

Depending on the similarity of a second operating process with different first operating processes (e.g. variants of the operating actions), a selection of the measure, determining or adjusting a parameter of the measure, and / or reducing a measure, in particular with regard to its intensity or deactivating a measure (e.g. such , which is normally active or should be activated according to the occupant's settings). This can, for example, ensure that no disadvantages arise from less effective or at least partially counterproductive measures.

Depending on the degree of similarity between the second operating process and one or more different first operating processes, for example, measures of different types and/or measures with different or differently adapted parameters can be carried out.

The measure may be a proactive measure. The measure can be a measure specific to the specific occupant, occupants with a specific personal profile, and/or to a specific type or variant of the second operating process and/or to the specific (respective, specific operating process). Different (each appropriate, expedient) measures can therefore be carried out for operating operations of the same type, which are carried out or (initially only) intended by a second driver and a second passenger.

Several measures, in particular an appropriately selected combination and/or sequence of measures, can be carried out.

The execution of the at least one measure can be carried out in a predetermined dependency, in particular in accordance with a predetermined mathematical context, from the steps described in Ver drive determined information. For example, the selection of the measure, determination or adjustment of a parameter of the measure, a reduction or deactivation of a measure can take place in a dependency, for example characterized by the mathematical context, on one or more pieces of information determined in the method.

Such a mathematical connection can characterize a mathematical function, law, rule, regulation, formula and/or relation. This can, for example, be characterized, in particular represented, by parameters and/or coefficients of a predetermined (computer-implemented) function. For example, the mathematical relationship, in particular depending on a large number of detected occupant errors, in particular in the respective first vehicle or in a backend, can be determined and then transmitted to the respective second vehicle (directly or indirectly) (also to be understood: activated in the vehicle become). The transmission can be carried out using a data connection, in particular mobile radio technology, for example according to a standard of at least 4G, 5G. The at least one mathematical context (transmitted to the respective vehicle) and used in this (e.g. as a configuration file) to carry out the measure. The measure can, but does not have to, be controlled from the first vehicle or backend.

The measure can be, for example, a measure to avoid a possible, in particular expected (second) occupant error and/or to reduce a consequence of a particularly expected (second) occupant error.

Particularly preferably, the measure can relate to a specific second operating process or a second operating process of a specific type. The measure can also relate to the operating process of a specific occupant (also referred to as the second occupant) or an occupant from a specific group of people. For example, one (specific) measure is implemented for specific second operating processes. Particularly preferably, it is a specific measure determined for the specific operating process, e.g. determined or adapted for the specific operating process. There can be measures tailored to the identified or predicted operating processes. The process can be carried out dynamically. The effect of the process may therefore differ from or go significantly beyond a general improvement of the vehicles.

In particular, the operating process of the (first or second) vehicle includes an interaction of the vehicle with the occupant or the occupant of the vehicle.

An occupant error or a common error can be recognized, for example, by the fact that an occupant has pressed the undo button one or more times in a menu item (e.g. for an operation in a menu) without the occupant having previously done so (e.g. in the operating menu). caused or carried out an action (which may have been recognized as meaningful). The occupant error can possibly also be recognized by a subsequent action (which is obviously unsuccessful or does not correspond to the occupant's expectations).

An occupant error can, for example, be a specific operating pattern or a sequence pattern of actions by the occupant (as part of an operating process) that is subsequently assessed as “undesirable”. Such patterns can, if necessary, be recognized and taken into account in relation to a specific inmate or in relation to a group of inmate.

In particular, the second vehicle is a different vehicle than the first vehicle. Preferably, the second vehicle may be a similar vehicle to the first vehicle. For example, the second vehicle can be a vehicle with the same or similar functionality, application, intended use as the one or more first vehicles. In particular, the second vehicle can be a vehicle of the same or similar design as the one or more first vehicles. Exceptionally, the second vehicle can be the same vehicle as the first vehicle. In particular, the second vehicle can correspond to a first vehicle at a later time interval. The method can also be carried out in such a way that a first vehicle is a second vehicle at the same time or at a later point in time or is handled as such.

In particular, a first vehicle and/or a second vehicle are directly or preferably indirectly (e.g. via one or more further devices, e.g. a wireless connection) networked vehicles. The method can be carried out (at least partially or completely) with the system, in particular comprising one or more first vehicles and/or a second vehicle and/or a backend server.

As the occupant (also and equally the occupant), in particular as a driver, passenger or user of a (possibly specific) vehicle, e.g. a user who regularly uses the vehicle or is recognized as such and/or (e.g. by a Vehicle access system, key, etc.) is logged in, can be understood. Therefore, a distinction can be made between the one or more first occupants and a second occupant, corresponding to a first vehicle or to the second vehicle.

Preferably, the at least one occupant error can be a typically human error, e.g. a careless error, an attention error, a memory error, a skill error, perception error, etc. The occupant error can be unintentional, erroneous, different from what was desired and/or in the expectation of another the result of the action carried out by the occupant. In the context of this document, an “incorrect operating procedure” is collectively referred to as an operating procedure that or the result of which does not correspond to the motivation or expectations of the occupant. This can be a technically correct or flawless process recorded or carried out by the vehicle. In particular, the (first and/or second) occupant errors are errors that are not associated with a technical error in the respective vehicle system. The second (e.g. initially only potential or expected) occupant error can be of the same type as the first error.

Preferably, the method includes detecting the one or more occupant errors. These can be recognized as specific occupant errors and/or occupant errors of a specific type and/or operating errors corresponding to a specific pattern. In particular, the method at least primarily includes the detection of human or human-specific errors. These may be recognizable as such and taken into account (in particular in deviation from any other effects or defects). These can be occupant errors that are specific to human nature or human-specific weaknesses (e.g. in contrast or comparison to errors, defects or weaknesses in vehicle systems).

Preferably, for a specific (already taking place or predicted) second operating process or a second operating process of a specific type, a specific measure can be determined and, in particular, implemented in a forward-looking manner. The measure can support the at least one (second) occupant of the at least second vehicle in that the, in particular certain (second) occupant errors and/or occupant errors of a specific type can be at least partially avoided or their consequences reduced. Preferably, the method can be carried out entirely or predominantly automatically, dynamically, and/or in an occupant-specific or occupant group-specific manner. In the simplest case, groups of occupants can be taken into account that are characteristic of drivers who drive the vehicle predominantly manually, drivers who predominantly drive the vehicle automatically, passengers in the front passenger seat, passengers in a back seat, etc. The method can take into account (at least in the result) that drivers and passengers cause different types of operating errors or operating errors with different probability or with different consequences (e.g. when operating a navigation system).

Executing a measure can include selecting a measure, determining or adjusting one or more parameters of the measure, initiating and/or directly or indirectly effecting the measure, e.g. with appropriately equipped means of the respective vehicle. Means of the vehicle set up in this way form a further aspect of the invention.

The method can be made executable in such a way that a (e.g. appropriate) measure is determined and initiated even before the (second) occupant of the second vehicle makes or possibly can make a (certain) passenger error. A measure can already be determined and, in particular, initiated before a (certain) second operating procedure by the (second) occupant begins or a (certain) occupant error can occur.

In particular, it can be recognized (in particular predicted) that a second operating operation is being carried out (or will be carried out) on the (second) vehicle, which is at least a previous first operating operation, which is at least partially affected by one or more, in particular mutually similar, occupant errors sufficiently similar, in particular having at least a certain degree of similarity to this or these.

The method particularly preferably includes a comparison of a large number of the first operating processes and/or first occupant errors with one another. The degree of similarity between these, including one another, can be determined and taken into account. There may be an accumulation of (among other) similar groups of operating procedures and (among each other) similar occupant errors (e.g. compared to an average value or a statistical value). be recognized and the corresponding frequency information taken into account. If (particularly) similar occupant errors are detected during (particularly) similar operating procedures, in response to this, a specific or adapted (ie also specific for this and/or or a measure tailored to this purpose.

Each of the similarity measures described in this document (also to be understood as “degree of similarity and/or affinity”) can preferably be an objective one, assigned according to certain, predetermined or adaptively selected criteria (e.g. by means of an appropriately set up pattern recognition), in particular quantifiable, measure of action. Qualitative and/or quantitative parameters or patterns of operating processes or occupant errors can also be compared or evaluated according to a specific criterion.

A similarity within the meaning of this document can also take into account, include or be a dissimilarity. For example, said similarity measure can alternatively or additionally take into account a dissimilarity measure.

For example, the presence of at least a certain level of similarity (e.g. a minimum level of similarity) can also be understood as a dissimilarity that is below a (certain) threshold value (e.g. as a maximum level of dissimilarity).

For example, “at least a certain level of similarity” is to be understood as a similarity that exceeds a certain threshold and/or as a dissimilarity that falls below a certain threshold.

In an exemplary case, a dissimilarity measure can be determined (e.g. using a negative classifier) or recognized by applying a dissimilarity criterion if the dissimilarity is below a certain threshold value. This can also be taken into account as a reciprocal variable of similarity, for example by forming a reciprocal variable. For example, a measure, a specific parameter of the measure or a proactive measure can be implemented primarily or only if no excessive dissimilarity in the operating processes is detected.

A similarity measure or a dissimilarity measure can (each) be evaluated on the basis of several (including different, multi-dimensional) criteria. For example, this can be done by comparing amplitudes, patterns (e.g. the operations to be compared), and/or the time functions according to which different parameters change.

In particular, a similarity measure and/or a dissimilarity measure can be used, for example in relation to operating processes (various parameters or patterns of operating processes), in relation to occupant errors (various parameters or patterns of occupant errors), and/or in relation to occupants (various parameters or patterns relating to the person of the occupant) are identified and taken into account.

The similarity measure and dissimilarity measure can also be determined or taken into account using different procedures (which can be viewed as part of the procedure). For example, using any method or a predetermined criterion, e.g. similarity criterion and/or dissimilarity criterion, it can be recognized or a conclusion drawn that at least a certain (sufficient) degree of similarity and/or at most a certain (e.g. maximum permissible) degree of dissimilarity is present. This can also be done without determining a (e.g. explicit) similarity measure or dissimilarity measure.

For different similarity measures (e.g. when detecting different similarity measures between operating processes, occupant errors, occupant personal profiles, etc.), different measures or measures with different parameters are preferred, e.g. measures of different types, measures at different times and/or with different intensities, effects.

For example, a second operation can be recognized that has a particularly high level of similarity (or a particularly low level of dissimilarity) with a large number of first operations, each with a particularly high level of similarity (or a particularly low level of dissimilarity) with occupant errors recognized, especially predicted. In response to this, in particular depending on the information determined, when executing the measure it can be assumed with particular certainty that a measure is necessary and/or a specific measure will actually be the appropriate measure. The risk of an unnecessary or disruptive measure can be reduced. This can also be done in advance. The selection of the measure and/or that Determining or adjusting the parameters of the measure can be done particularly precisely.

The corresponding data from a (large) number of the first vehicles is preferably used. The system (also to be understood as one or more, in particular at least partially or temporarily networked devices) can therefore include many, for example thousands, millions of vehicles.

For example, it may have been determined (e.g. by a backend server or by the vehicle or by another (possibly identical or similar vehicle) that the first operating operation of the respective occupant was at least partially reversed. In response to this, it can then be determined that the first operation had an occupant error.

A first operating process may therefore have been identified which (possibly often) has an occupant error. It can then be recognized (in particular predicted) that a second operating procedure is being carried out on the vehicle, which is similar to the first operating procedure (and therefore may also have a possible occupant error). The (possible) occupant error can include an occupant error when using and/or operating the vehicle and/or when interacting with the vehicle (also to be understood as involving a vehicle functionality, e.g. a navigation system).

An operating process can include or be the actuation of a control element (such as a button, rocker, a multi-dimensionally movable control or input element, a rotary knob, a slider, etc.).

Preferably, the operating process can include or be an input using a sensitive screen (e.g. a touchscreen or a non-contact interactive screen), using gestures, e.g. swiping gestures, finger gestures or gesture recognition, however configured.

An operating process can include or be the selection or operation of a menu item in a user menu of the vehicle, e.g. typing, setting or fine-tuning values, operating a virtual controller.

An operating process can also include an, in particular non-contact, action or interaction of the occupant with the operating interface of the vehicle.

The (first and/or second) operating process preferably includes one or more fine motor actions and/or actions of the occupant that can be carried out with individual fingers. Such operating procedures can typically involve multiple or different occupant errors, for example in comparison to pressing a button.

For the vehicle, so to speak from the visual perspective of the vehicle, the operating process can correspond to reading in or recognizing a specific operating process or an input and/or a result desired by the occupant.

The method may include determining one or more first parameters related to the first operating process, and determining one or more second parameters related to the second operating process. The one or more parameters can describe the respective operating process (e.g. the control element that is activated; the menu item that was selected as part of the operating process, etc.).

Particularly preferably, one or more parameters that are characteristic of a sequence of the operating process are determined and taken into account. One or more such parameters, in particular a change characteristic of one or more parameter values, e.g. characterizing an interaction with the operating interface, can be determined and taken into account over time.

A similarity measure (also to be understood as a value of a similarity indicator) for the similarity of the first and second operating processes can then be determined based on the one or more first parameters and based on the one or more second parameters. Furthermore, based on the determined similarity measure, it can be determined whether the second operating process has a (certain, sufficient) similarity, in particular a similarity measure, to the first operating process or not (and if necessary or the second operating process corresponds to the first operating process, for example in terms of its type and/or or parameter). In particular, a second operating process with a possible occupant error can be recognized (in particular predicted) in a precise and reliable manner. The method may further include, in response to the detected second operation (which is similar to or corresponds to the first operation according to the similarity measure), executing at least one action with respect to a possible occupant error in the second operation. In particular, executing the measure can involve a determination, in particular an outcome include choosing, determining or adapting the measure (e.g. several variants, characteristics, methods, intensities, etc.). In other words, it can be a special and/or dynamically determined measure (tailored, so to speak, to a possible second occupant).

The measure can be aimed at changing the operating logic and/or a parameter of the user interface of the vehicle at least temporarily or only temporarily. Alternatively or additionally, the measure can be aimed at avoiding the possible occupant error during the second operating process and/or an effect of the possible occupant error on the vehicle. Alternatively or additionally, the measure can include an output to the occupant of the vehicle, the output being, for example, a suggestion for changing the behavior of the vehicle (in particular also to be understood as the behavior of a system or a functionality of the vehicle), in particular the behavior of the information input, Information output, user interface of the vehicle (where the change can be made or confirmed, for example, by the occupant of the vehicle).

The method thus makes it possible to reliably detect (in particular predict) a possible occupant error when operating a vehicle and to avoid and/or at least partially compensate for the occupant error.

The method can be executable (also: parameterized, optimized) with the objective or can be carried out in such a way that (at least statistically) the probability of operating errors and/or the consequences thereof decreases. In doing so or as a result, the comfort, effectiveness, reliability and/or success when operating, in particular when operating, a (e.g. the second) vehicle can be increased.

In particular, the method and/or the (corresponding) system can be aligned and/or optimized and/or controlled or regulated so that comfort, effectiveness, reliability and/or success in operating a vehicle, at least in a statistical view, one or more objective ones or to increase objective measures or change them towards a target value, in particular towards a maximum. Success is to be understood in particular as an improved correspondence between a result of the operating process and the motivation and/or expectations of the occupant. In particular, the method can be carried out in such a way that a qualitative and/or quantitative key figure (simplified: number and/or severity) of further occupant errors is reduced. For example, the process can provide for appropriate processing of the data or the system can be set up accordingly.

The at least one measure may include: changing an information output channel in which information is output to an occupant of a vehicle. The information channel can be changed (e.g. only temporarily and/or occupant-specific or person-specific) (e.g. depending on the determined probability and/or consequence of an operating error during the second operating process). The change can take place, for example, from or towards a visual, auditory, haptic and/or kinesthetic information channel.

Alternatively or additionally, the at least one measure can include: changing an information input channel that is activated to an occupant of the second vehicle for operating input or information input, in particular is offered for inputting information; and/or changing the information input channel via which an input from the occupant of the vehicle is at least primarily processed, in particular towards a voice input, gesture control, touchscreen and/or physically movable control element.

Alternatively or additionally, the at least one measure can include: changing a parameter of an output and/or an input option in connection with the respective operating process.

There can be a scope of the output; and/or a display type and/or level of detail of the output; and/or a position of the output that can be perceived by the occupant can be changed. For example, a contrast, symbol, and/or font and/or size, and/or position and/or displacement of virtual controls can be changed.

The method may include determining one or more driving situations and/or context parameters in relation to a boundary condition and/or in relation to a context of the occupant error in the first operating process. In a corresponding manner, one or more context parameters can be determined in relation to a boundary condition and/or in relation to a context of the second operating process.

The second operating process (in which another occupant error may occur) can then be performed with increased reliability depending on the one or more driving situations tion parameters and/or context parameters are recognized (in particular predicted). Alternatively or additionally, the measure can be determined and/or effected in relation to the possible occupant error during the second operating process depending on the one or more context parameters. The current and/or previous and/or predicted driving situation for the near future can be taken into account.

In the context of this document, the term “driving situation” can be understood, for example, as a specific situation characterized by an arrangement, action, or interaction of road users or by certain driving parameters of road users.

In particular, the meaning of the term “situation” or “driving situation” differs from the meaning of the term “traffic situation” that is often used colloquially, which rather summarizes general categories such as “free traffic”, “dense traffic”, “slow-moving traffic”, “ Traffic jam”, “end of traffic jam” etc. corresponds.

In particular, a driving situation is (rather) characterized by a specific pattern (also to be understood as a data pattern), e.g. a pattern characterizing an arrangement and/or speed of the objects and/or a pattern of the parameters of the driving situation. The driving situation can also be characterized by a spatial pattern of so-called open spaces in the area surrounding the vehicle. For example, a trigger condition can relate to such features or corresponding parameters of the driving situation.

• - eine (bestimmte) räumliche Verteilung der Verkehrsteilnehmer und/oder der Bewegungsparameter der Verkehrsteilnehmer, insbesondere ein Anordnungsmuster der Verkehrsteilnehmer in der Umgebung des (realen) Fahrzeugs,
• - eine (bestimmte) räumliche Verteilung unbeweglicher Objekte in der Umgebung des Fahrzeugs,
• - eine relative Position und/oder Bewegungsparameter zu bestimmten Arten von Spurmarkierungen, Verkehrszeichen, Ampeln (nicht notwendigerweise zu bestimmten Ampeln, etc.),
• - eine Information über die Vorfahrt des Fahrzeugs, insbesondere gegenüber bestimmten Verkehrsteilnehmern und/oder Verkehrsteilnehmern, die tatsächlich oder zumindest potentiell aus bestimmten Richtungen, z.B. einer querenden Straße von rechts oder von links kommen oder kommen können,
• - eine Information zu einer, z.B. einen Grenzwert überschreitenden, Handlung eines Verkehrsteilnehmers in der Umgebung des Fahrzeugs, wie z.B. ein Hupen, Lichthupen, Drängeln, Überholen des Fahrzeugs, ein Überholversuch und dergleichen.

The at least one driving situation can preferably be characterized by one or more features listed below:

• - a (certain) spatial distribution of the road users and/or the movement parameters of the road users, in particular an arrangement pattern of the road users in the area surrounding the (real) vehicle,
• - a (certain) spatial distribution of immovable objects in the area surrounding the vehicle,
• - a relative position and/or movement parameter to certain types of lane markings, traffic signs, traffic lights (not necessarily to specific traffic lights, etc.),
• - information about the right of way of the vehicle, in particular towards certain road users and/or road users who are actually or at least potentially coming from certain directions, e.g. a crossing street from the right or from the left,
• - Information about an action by a road user in the vicinity of the vehicle, for example exceeding a limit value, such as honking, flashing lights, jostling, overtaking the vehicle, attempting to overtake and the like.

Furthermore, the driving situation can be characterized by one or more parameters in connection with relevant traffic rules, traffic signs, right-of-way, traffic lights and/or traffic light phases.

The at least one driving situation can preferably be a driving situation that exceeds certain limit values or a driving situation that is characterized by parameters that exceed certain limit values. For example, the at least one driving situation can be a driving situation with an undesirable or dangerous approach to an object or a road user, an acceleration value that exceeds a limit value, an undesirable arrangement with other road users, etc.

The driving situation can be a special situation (e.g. that occurs comparatively rarely) or a dangerous driving situation, e.g. a driving situation for which an increased risk is determined or assumed.

The at least one context parameter can include or be a context for the perception (or perception context) of the occupant. For example, a context parameter can be characteristic, in particular representative, of lighting conditions, in particular a strength and/or direction of the lighting, changes in the lighting over time (e.g. driving along avenues, bridges, winding streets, etc.) and/or noise conditions, in particular noise other vehicles, noise from one or more other occupants (e.g. certain voices), radio, etc.

Thus (e.g. an effective and targeted) measure can be carried out to prevent a second operating error that can arise due to a temporarily disturbed or slightly changed perception of the information and/or a consequence of such a second operating error.

The method may include determining at least one consequence parameter in relation to a consequence and/or an effect of the possible occupant error in the second operating procedure. It can then be dependent A measure can be determined or effected by the consequence parameter in relation to the possible occupant error in the second operating process. In this way, the reliability of the operation of the vehicle can be further increased.

As part of the method, first occupant information can be determined in relation to the (first) occupant and/or in relation to the group of people that belongs to the occupant who caused the occupant error in the first operating process. Alternatively or additionally, the occupant's membership in one or more groups of people can be understood as a personal profile or parameters of a personal profile of the occupant. Alternatively or additionally, second occupant information can be assigned in relation to the (second) occupant and/or in relation to the group of people to which the occupant belongs, in particular (e.g. according to the occupant's own information, or automatically through pattern recognition) through which the second operating operation was effected or is caused, can be determined. The recognition of the second operating process and/or the determination and/or effecting of the measure can then take place depending on the first and/or depending on the second occupant information. In particular, the similarity between the first operating process and the second operating process can be determined with increased accuracy based on the first occupant information and based on the second occupant information.

The method may include determining input from the occupant of the vehicle. In the input, the occupant can, for example, specify how to react to a possible occupant error (e.g. by automatically correcting the possible occupant error and/or by issuing a message regarding the possible occupant error). A measure dependent on the occupant's input can then be determined and/or implemented.

As part of the method, data may have been determined (e.g. in advance and/or in advance) in relation to a plurality (e.g. hundreds, thousands, millions) of first operating procedures or the operating errors recognized in these. These can be determined using a backend server, stored and/or processed by it. The first operations can be carried out on one or more different (possibly identical) vehicles. Based on the data, frequency information relating to the frequency of the occupant error in the plurality of first operating processes can be determined or has been determined. In particular, it can be determined or has already been determined (at the time interval at which a second occupant error can occur on the second vehicle) how frequent the occupant error, in particular a specific occupant error or an occupant error of a specific type, is during the first operating processes. The measure in relation to the possible occupant error during the second operating process can then be effected depending on the frequency information determined.

For example, the selection, determination or adjustment of a parameter of the measure takes place depending on the frequency information. For example, no action can be taken if the frequency of the occupant error is less than a certain frequency threshold. On the other hand, a measure (e.g. a measure of a certain type and/or a measure with a certain parameter value range) can possibly only be implemented or primarily if the frequency of the occupant error is greater than the specific frequency threshold value. Especially by taking frequency information into account, the comfort and reliability of operating the vehicle can be further increased.

According to a further aspect, a system for supporting an occupant when operating, in particular when operating, a vehicle is described. The system is set up to determine an occupant error during one or more first operating processes of a first vehicle; and is set up to recognize or predict a second operating operation of a second vehicle; wherein the second operation has at least some degree of similarity to the one or more first operations; and then: take an action regarding a possible occupant error in the second operation.

The system preferably comprises one or more first vehicles, at least one second vehicle and/or a backend (also to be understood as a cloud, a server, a portal). One or more first vehicles are preferably designed for a direct or indirect operative connection with the at least one second vehicle and/or the backend. The one or more first vehicles can be designed to determine parameters characterizing one or more (first) occupant errors, e.g. type of occupant errors, type of corresponding operating processes, etc.

Furthermore, the one or more first vehicles can be designed to transmit the corresponding data (eg when executing the active connection) to the backend (BE). The backend can be designed to process the data from a plurality of the first vehicles, in particular one or more frequency information (e.g. about several different groups of operating procedures and occupant errors). Based on the processed data, in particular based on the frequency information, a measure can be implemented (at least partially in the backend and/or at least partially in the second vehicle).

In particular, the system can also include an operating system of the vehicles and/or data interfaces set up to carry out the method, for example hardware-related interfaces. The system, e.g. the majority of the first vehicles, second vehicles and/or backend can be operated by the same provider, operator, service provider and/or a standard or convention designed in accordance with the invention. The one or more first vehicles and the one or more second vehicles may be offered directly or indirectly by the same manufacturer or operator.

According to a further aspect, a computer program product comprising a computer program and/or data for executing the computer program is described, wherein the computer program product is designed to carry out the method described in this document when it is executed on at least one computing unit.

The computer program product can be designed as an update of a previous computer program, which includes the parts of the computer program or the corresponding program code, for example as part of a functional expansion, for example as part of a so-called “remote software update”. The computer program product includes in particular a medium readable by the data processing device, on which the program code is stored, or at least an encrypted file. According to a further aspect, a program product is described that includes an authorized access right to stored data of the computer program product.

It should be noted that the methods, devices and systems described in this document can be used both alone and in combination with other methods, devices and systems described in this document. Furthermore, any aspects of the methods, devices and systems described in this document can be combined with one another in a variety of ways. In particular, the features of the claims can be combined with one another in a variety of ways.

• 1 ein Beispiel für das System. Dieses umfasst mehrere erste Fahrzeuge 10, 11, 12, ein Backend (BE) sowie zumindest ein zweites Fahrzeug 20, 21, 22; und
• 2 ein Ablaufdiagramm eines beispielhaften Verfahrens 100 zum Betrieb eines oder mehrerer Fahrzeuge 11, 11, 12 bzw. 20, 21, 22, insbesondere eines System umfassend das ein oder mehrere Fahrzeuge.

In the following, the invention is described in more detail using exemplary embodiments without restricting the generality. All features of the exemplary embodiments can be combined with one another and/or with the features of the claims. Show:

• 1 an example of the system. This includes several first vehicles 10, 11, 12, a backend (BE) and at least one second vehicle 20, 21, 22; and
• 2 a flowchart of an exemplary method 100 for operating one or more vehicles 11, 11, 12 or 20, 21, 22, in particular a system comprising one or more vehicles.

The one or more vehicles 11, 11, 12 or 20, 21, 22 can (each) include a user interface or HMI (= human-machine interface), which is designed to operate or operate an occupant of the respective vehicle Vehicle 10, 11, 12, 20, 21, 22 to detect or recognize. However, the operating interface can also be understood as an information input device and information output device.

Furthermore, the vehicle 10, 11, 12, 20, 21, 22 includes a (control) device that is set up to operate the vehicle 10, 11, 12, 20, 21, 22 depending on the recognized operating process (e.g. the input or output of information, the playing of an audio signal, etc.).

It can be recognized that a specific occupant of a vehicle 10, 11, 12, 20, 21, 22 and/or an occupant who belongs to or is assigned to a group of people, in particular to a particular extent that overlaps a statistical measure, tends to to make, in particular to repeat, certain occupant errors, in particular with regard to the operation of the vehicle 10, 11, 12, 20, 21, 22.

For example, an operating error can consist in the fact that at least one occupant or occupant from a group of people forgets to carry out a certain operating action on their vehicle (in good time), does not activate the intended option, selection or effect, or “clicks” or a certain one Operating action is carried out incorrectly (e.g. in the wrong direction).

The system described in this document can (also) be set up to recognize an occupant error as such. An occupant error can be recognized, for example, by the occupant revoking the (incorrect) operating procedure themselves, correcting it or doing it too late (much later). as a corresponding causal event).

To detect an incorrect or unsuccessful operating process, data relating to the vehicle 10, 11, 12, 20, 21, 22 (e.g. relating to the operation of the vehicle 10, 11, 12, 20, 21, 22) can be evaluated . If necessary, a (machine-learned) artificial intelligence can be trained to recognize occupant errors as such.

For example, it can be recorded that an occupant (statistically) and/or a specific occupant each time selects a specific menu item on the user interface 102 of a vehicle 10, 11, 12, 20, 21, 22 and then each time, several times in a row, for several operations, or sufficiently frequently, the selection of the menu item is reversed.

Alternatively or additionally, consequences (of any kind or of one or more specific types) of the occupant's actions, e.g. in different operating procedures, situations, contexts, etc., can be determined. There may be consequences that (particularly in hindsight or according to a subsequent evaluation of the data) lead to an unexpected result, with none of one or more predetermined criteria (defined as “correct”, “positive”, or “not incorrect”) and/or patterns have led to sufficiently consistent or similar, so to speak strange or obviously undesirable results.

Alternatively or additionally, a deviation of the operating process of a specific occupant from a statistically typical operating process and/or deviation of the occupant, e.g. a typical occupant user and/or an occupant who belongs to the same group of people, can be determined and taken into account. By analyzing data relating to the operation of vehicles 10, 11, 12, 20, 21, 22, it can thus be made possible to detect an occupant error in the operation of a particular vehicle 10, 11, 12, 20, 21, 22 more reliably to recognize and/or predict in a manner (as such or as an operating error of a certain type or with a certain consequence).

If necessary, an occupant error from the past can be evaluated “retroactively” as an occupant error, in particular as a specific occupant error or an occupant error of a certain type, and in the future, e.g. the next time a pattern with the recognized boundary conditions and/or present in the identified situation can be avoided.

If an occupant error is detected when operating a vehicle 10, 11, 12, ... 20, 21, 22, at least one preventive measure (e.g. the most expedient or appropriate possible) can be initiated. The measure can be determined depending on one or more patterns (e.g. depending on a pattern of actions that does not lead to an expected or satisfactory result).

If necessary, several alternative or additional measures can be selected. As a measure, occupant information (suitable, e.g. to be issued preventively) can be generated. Particularly preferably, preventative occupant information (e.g. dependent on a recognized pattern) can be generated, with which a new occupant error or a corresponding consequence can be prevented. Furthermore, a measure can involve a change or a targeted adaptation of one or more properties of the operating interface and/or the functionality of the vehicle 10, 11, 12 and/or another similar vehicle (in particular also to be understood as a vehicle of a similar type, equipment , age, technologies used, etc.) 20, 21, 22.

For example, from an evaluation of several operating processes by a specific occupant or an occupant who belongs to a specific group of occupants (also to be understood as a group of people to which an occupant can more or less be counted), it can be determined that a vehicle 10, 11, 12, . .. 20, 21, 22 is incorrectly operated in a certain way, in particular it is incorrectly interacted with in a certain way. This can be recognized, for example, by one or more respective patterns. For example, it is recognized that a respective operating process is carried out differently by the respective occupant or that the result of the operating process is corrected by the occupant. A measure can then be generated (partially, predominantly or completely automated), in particular in the form of information for the occupant as a suggestion, to change the corresponding meaning or the functioning of the operating interface and/or a parameter of the corresponding functionality.

The measure can be initiated for the same specific occupant and/or preferably for another occupant, in particular an occupant who is assigned to the same group of people.

In particular, from occupant errors (e.g. if these are recognized as failures) of the same occupant and/or one or more other occupants (also automatically) take a measure to make an appropriate or appropriately selected change to a vehicle 10, 11, 12, ... 20, 21, 22, ... (also to be understood as: a device or functionality of the vehicle) , in particular the user interface of the vehicle 10, 11, 12, ... 20, 21, 22, ... are generated. The change to the vehicle 10, 11, 12, ... 20, 21, 22, ... can then possibly be implemented depending on the consent of the respective occupant to carry out at least one step of the method or automatically.

Alternatively or additionally, a measure can be aimed at avoiding or reducing a consequence of the occupant error. If necessary, a repetition of the occupant error can be permitted, but at least one measure can be taken to avoid a consequence associated with the occupant error (on the vehicle 10, 11, 12, ... 20, 21, 22, ...) or to get prepared.

As part of the process described in this document, it can be recorded (also to be understood as a type of “observation”) that certain occupant errors and/or certain frequency distributions of (certain) occupant errors depend on a characteristic of the occupant's personality, e.g. the group of people, to which an inmate can be assigned.

Alternatively or additionally, the occupant's membership in one or more groups of people can be understood as a personal profile or parameters of a personal profile of the occupant.

The detection or interpretation of an occupant error and/or a measure to avoid the occupant error or to avoid the repetition of the occupant error and/or a measure to reduce a consequence of an (expected) occupant error can therefore be dependent on the recognition of a group of people who are the Inmate is heard or assigned automatically, e.g. based on his information, input and/or behavior. Thus (at least statistically) the operation of a vehicle 10, 11, 12, ... 20, 21, 22, ... can be further improved.

2 shows a flowchart of an exemplary method 100 (possibly computer-implemented) for supporting an occupant of a vehicle 10, 11, 12, 20, 21, 22.

The method 100 includes determining 101, in particular recognizing, an occupant error when operating one or more first vehicles 10, 11, 12, in particular a part of the vehicle fleet (e.g. test or customer fleet). These can be recognized as such, especially in retrospect or post-factum.

The occupant errors relate in particular to operating processes or are made in connection with the operating processes (generally: usage processes) of the first vehicles 10, 11, 12, ... by their respective, in particular current, occupants. These can be recognized as such, assigned to the specific operating processes and/or the data on the occupant error can be processed or taken into account together with the data from the operating processes. The occupant errors can be distinguished from other, e.g. technical, errors, for example by recognizing that there was no technical error or that the influence of a technical error was comparatively small.

Furthermore, the method 100 includes the recognition 102, in particular the prediction, of a second operating operation of a second vehicle 20, 21, 22, wherein the second operating operation has a (sufficient) similarity, in particular at least a certain degree of similarity, with the one or more first operating operations. A quantitative or finely graded similarity measure can be determined and taken into account. For example, the selection of a measure (from several possible ones), and/or determining or adjusting the parameters of the measure and/or initiating the measure can (also) be a function of the similarity measure. For example, if the similarity measure is relatively low, a less intensive measure can be initiated.

For example, a second operating process can be recognized for which a similarity (e.g. estimated by means of pattern recognition or by means of a pattern comparison), in particular a similarity measure (e.g. an indicator which is greater the more similar the operating processes are), has a value that is greater than a certain threshold value.

The method 100 further includes executing (in particular also to be understood as initiating or causing) 103 a measure in relation to an (initially possible or to be considered possible) occupant error in the second operating process. This may be the second occupant error of a second occupant that is different from the one or more first occupants (the one or more first operations).

This results, at least in the result of the method 100, in the effect that a possible Inmate errors by a second occupant are prevented before the occupant has even made an occupant error or before the occupant has started a specific operating procedure. An operating process can be related to inputting and/or outputting information using the respective vehicle.

Executing the measure can, for example, be a selection, determining or adjusting the parameter of the measure (appropriate in the respective case), determining a precondition for executing the measures, initiating the measure, and/or executing the measure, in particular pending of the precondition being met.

The measure is carried out in particular in response to one or more previous steps of the method 100, in particular in a predetermined dependency on the information determined. This is preferably done dynamically and, so to speak, in a manner tailored to the relevant case. Furthermore, the method 100 may also include initiating (effecting) a measure that changes the behavior of the second vehicle, in particular temporarily and/or permanently. This can be done in relation to certain inmates and/or groups of inmates who are assigned to certain groups of people. The measure can (also) be an update of the at least one second vehicle that is tailored to specific occupants or occupants belonging to specific groups of people (i.e. tailored to them).

The method 100 can detect 101 (the term “recognize” can in particular also be understood as predictive recognition or prediction) of a second operating process on the vehicle 10, 11, 12, 20, 21, 22, which is similar to at least one previous one has first operation; where the first operation had an occupant error. A second operating operation on the vehicle 10, 11, 12, 20, 21, 22 can therefore be recognized, which is at least similar to a first operating operation that had an occupant error (e.g. clumsiness or incorrect operation of the occupant). This can be viewed as an indication (particularly depending on the frequency of the occupant error in a large number of first operating processes) that an occupant error could also be present in the second operating process.

The method 100 may further include, in response to the detected second operation, effecting 103 an action regarding a possible occupant error in the second operation.

In particular, a measure can be implemented 103 in order to prevent (preferably proactively) that the second operating process also has the occupant error (in a simplified example by issuing a warning regarding the possible occupant error). Alternatively or additionally, a measure can be implemented to avoid or at least reduce a possible consequence and/or effect of the occupant error on the vehicle 10, 11, 12, ... 20, 21, 22, ... (e.g. by taking into account the occupant error during the operation of the vehicle 10, 11, 12, ... 20, 21, 22, ...). In this way, the comfort and reliability of operating a vehicle 10, 11, 12, ... 20, 21, 22, ... can be increased.

The method 100 can in particular also be understood as a method for handling occupant errors. The method 100 can determine an occupant error when operating a first vehicle 10, 11, 12, ... 20, 21, 22, ... during one or more first operating processes on the first vehicle 10, 11, 12, ... 20, 21, 22, .... include. The method 100 can further include recognizing, in particular predicting, a second operating process on a second vehicle 10, 11, 12, ... 20, 21, 22, ... that has a certain (sufficient, e.g. a predetermined static or dynamic threshold value) has similarity measure with the one or more first operations. The second vehicle 10, 11, 12, ... 20, 21, 22, ... and the first vehicle 10, 11, 12, ... 20, 21, 22, ... can possibly be the same vehicle, or identical vehicles or possibly different (similar) vehicles.

The method 100 can include the detection 101 (e.g. in response to the detection 101 and/or depending on the parameters of the detected operating process), the determination 102, in particular the prediction, of a measure. The measure can serve to avoid the occupant error and/or to reduce a consequence of an expected occupant error in connection with the recognized or predicted second operating process. Determining, in particular predicting, a measure can include determining or adapting a measure, e.g. a variant or a parameter of the measure.

Furthermore, the determined measure, in particular determined predictively (e.g. only or primarily in the event that a specific occupant error is made as expected), can be carried out.

The identified measure and/or the initiation of the measure can be linked to a (certain) precondition. For example, the measure is only carried out when and if the precondition is met. This means that no possible disruption needs to be created during the (second) operating process, for example if the operating process will not be similar or it is recognized that the probability of the (second) occupant error being made by the (second) occupant is low. Such a precondition can be different, for example, for different occupant errors, for example for occupant errors with different (expected or maximum expected) consequences. The precondition can, for example, be dependent on a further increasing probability and/or dependent on the exceeding of a probability measure and/or a consequence of the occupant's error.

An occupant error is preferably predicted before (e.g. a few seconds or milliseconds before) its expected occurrence, in particular before the second operating process. This alone can save (valuable) time for determining or selecting and executing the measure.

For example, one or more suitable measures or variants of measures can be determined in advance (unnoticed by one or more occupants, in the background, so to speak) even with relatively small error probabilities (which are then only carried out if necessary, for example if the probability increases). Such a measure can include or be a measure for preparing a device of the vehicle for the operating error or a consequence of a (possible) operating error.

By determining a possible measure in advance, a (e.g. predictive) algorithm can be given the necessary time to predict an occupant error. If it is recognized that the precondition for a particular measure applies, then the measure can be selected and carried out.

An operation may include an action of the occupant with respect to the vehicle 10, 11, 12, 20, 21, 22 or an interaction of the occupant with the vehicle 10, 11, 12, 20, 21, 22. An occupant error can be an occupant error with characteristics of so-called human error.

As part of the method 100, one or more parameters of the driving situation and/or boundary conditions and/or the context can be determined and taken into account in connection with the occurrence of the at least one occupant error in the one or more first operating processes. In particular, one or more parameters of the driving situation and/or boundary conditions that precede the occurrence of the occupant error and/or one or more parameters of the boundary conditions that essentially exist during the occurrence of the occupant error can be determined and taken into account.

The one or more parameters can be stored in a memory, for example in a ring memory or in a comparable memory structure, and provided and/or further processed depending on the occurrence or detection of an occupant error.

In particular, one or more parameters of the boundary conditions, preferably a pattern of parameters of the boundary conditions, can be determined and taken into account in connection with the occurrence of the occupant error in the one or more first operating processes.

Preferably, data relating to the one or more parameters of the boundary conditions can be determined, compared and taken into account in a large number of first operating processes.

• - Beleuchtungsverhältnisse, insbesondere eine Stärke und/oder Richtung der Beleuchtung, Veränderung der Beleuchtung im Verlauf der Zeit (z.B. Alleenfahrt, Brücken, kurvige Straße, etc.); und/oder
• - Lärmverhältnisse, insbesondere Lärm anderer Fahrzeuge, Lärm von einem oder mehreren weiteren Insassen (z.B. bestimmte Stimmen), Radio, etc.; und/oder
• - eine Bewegung, insbesondere ein Bewegungsmuster, des Fahrzeugs 10, 11, 12, 20, 21, 22; und/oder
• - eine Bediensituation, z.B. ein oder mehrere Aktionen, die vor dem ersten Bedienvorgang erfolgen, und/oder ein oder mehrere Aktionen, die (zumindest wahrscheinlich) nach dem ersten Bedienvorgang folgen.

One or more parameters of the boundary conditions can be characteristic, in particular representative, of:

• - Lighting conditions, in particular the intensity and/or direction of the lighting, changes in the lighting over time (e.g. driving on avenues, bridges, winding streets, etc.); and or
• - Noise conditions, in particular noise from other vehicles, noise from one or more other occupants (e.g. certain voices), radio, etc.; and or
• - a movement, in particular a movement pattern, of the vehicle 10, 11, 12, 20, 21, 22; and or
• - an operating situation, for example one or more actions that take place before the first operation and/or one or more actions that (at least probably) follow the first operation.

Alternatively or additionally, a temporal and/or spatial change characteristic, in particular a gradient, such as a temporal and/or spatial gradient, of a (respective) parameter or a parameter value can be determined and taken into account as a parameter.

As part of the method 100, one or more parameters of the consequence of at least one occupant error in relation to the one or more first and/or second operating processes, in particular several variants of an operating process, can be determined and taken into account.

Preferably, the at least one measure is determined depending on one or more parameters of a corresponding consequence, in particular predicted in the context of the method 100. Depending on a predicted consequence, for example, you can react earlier and/or with a different measure.

For example, a measure is selected from a list of at least two different measures or different types of measures in connection with the at least one second operating process, possibly depending on the data relating to the one or more parameters of the consequences for the one or more first ones operations. In other words, a prediction of a consequence of a specific occupant error can take place depending on data from the one or more first operating processes.

Preferably, the data can be determined, compared and taken into account in relation to the one or more parameters of the consequence of a large number of occupant errors. For example, statistical accumulations of occurrences of certain or consequences of occupant errors of a certain type characterized by certain parameters, in particular with one or more certain criteria, e.g. patterns of corresponding first operating procedures, driving situations, contexts, etc., can be determined and taken into account.

Preferably, information about a number or frequency of occurrence and/or non-occurrence of the at least one (in particular a specific) occupant error is determined after and/or approximately simultaneously with the occurrence of the occupant error from a plurality of first operating processes. The at least one measure can then be determined in a second operating process depending on the information regarding a number or frequency of occurrence of the occupant error.

For example, information is determined that is characteristic of the number or frequency of occurrence of an occupant error, a specific occupant error or an occupant error of a specific type in a large number of first operating processes, in particular in connection with a specific parameter of the driving situations, boundary conditions, for example a specific one Patterns of the parameters of the driving situations and/or boundary conditions.

Alternatively or additionally, information characterizing the number or frequency of non-occurrence of an occupant error, a specific occupant error or an occupant error of a specific type can be determined in a plurality of first operating processes, in particular in connection with a specific, in particular the same, parameter of the driving situations and/or the boundary conditions, for example the specific patterns of the parameters of these.

As part of the method 100, one or more parameters of the second operating process can be determined, and based on this, at least one measure of similarity of the one or more parameters of the second operating process with one or more parameters of the one or more first operating processes can be determined and taken into account.

Particularly preferably, one or more patterns of the parameters of the second operating process can be determined and at least one measure of similarity of the one or more determined patterns can be determined and taken into account with at least one or more parameters of a first operating process. For example, a combination and/or a pattern of parameters that characterize a position and/or a driving profile of the occupant can be determined and taken into account.

As part of the method 100, information characterizing at least one occupant error in the one or more first operating processes can be determined and taken into account, in particular depending on the occupant and/or the group of people to which the occupant belongs or to which the occupant is assigned in the method.

The at least one measure, in particular a variant and/or a parameter of the measure, can be determined depending on a previously determined input or selection of the occupant.

In particular, it can be made possible for the occupant to decide (in advance) about a measure that should be carried out if a specific occupant error (e.g. an occupant error of a certain type) is recognized or predicted. For example, it can be made possible for the occupant to select, program or put together a measure (possibly configurable and designed by the occupant) from predetermined elements. For example, the occupant can be given the opportunity to remember information, such as a text, a keyword, etc Announcement, etc., to select, create or pre-configure.

In other words, the occupant can be given the opportunity to decide how he or she can possibly be helped in the event of a specific occupant error or an occupant error of a certain type.

The at least one measure can include a change to an operating logic and/or a parameter of the user interface of the vehicle 10, 11, 12, 20, 21, 22. For example, depending on a (statistical) accumulation of one or more specific occupant errors, the behavior of the respective vehicle 10, 11, 12, ... 20, 21, 22, ... in particular with regard to the operating logic and / or a parameter of the user interface of the Vehicle 10, 11, 12, ... 20, 21, 22, ... can be changed. For example, a menu structure of the vehicle 10, 11, 12, ... 20, 21, 22, ... can be changed (e.g. in several steps).

As part of the method 100, the at least one measure can include an output of occupant information with a specific suggestion for changing the behavior of the vehicle (e.g. at least one device or functionality of the vehicle) 10, 11, 12, 20, 21, 22. The occupant information can, for example, include a suggestion for changing the operating logic and/or a parameter for interpreting an input from the user of the vehicle 10, 11, 12, 20, 21, 22. The (then possibly effective) change in the behavior of the vehicle 10, 11, 12, 20, 21, 22 can be carried out depending on confirmation from the occupant.

A second vehicle 10, 11, 12, ... 20, 21, 22, ... can be set up to receive data relating to an occupant error described in this document directly or indirectly from the one or more first vehicles 10, 11, 12, . .. 20, 21, 22, ... to receive. In particular, this concerns data processed, for example, in the backend BE or as part of data processing DV. Furthermore, it can be configuration data corresponding to the method. For example, the configuration data can include or represent the mathematical relationship already described.

Furthermore, this document describes a method aimed at determining and/or providing data relating to an occupant error (e.g. as part of a first vehicle 10, 11, 12, ... 20, 21, 22, . ..), or to receive data relating to an occupant error and/or to use it to determine a measure (e.g. as part of a second vehicle 10, 11, 12, ... 20, 21, 22, ...).

Furthermore, this document describes a system (made up of one or more devices) that is set up to carry out one of the methods 100 described in this document.

In particular, the system is or includes one or preferably a plurality of first vehicles 10, 11, 12, ..., one or more second vehicles 20, 21, 22, ... and possibly a backend BE (in particular with data processing DV for aggregating the data from a large number of vehicles 10, 11, 12, 20, 21, 22. It can be a so-called distributed system. Depending on the result of the data processing or the aggregation of the data, the said mathematical relationship can be determined ( and used in vehicles F10, F11, F 12).

The system may be configured to perform one or more of the methods 100 described in this document. It can be a distributed system. The system can be designed according to one or more of the features of the method described in the document. The first vehicle 10, 11, 12, ... and the second vehicle 20, 21, 22, ... can be vehicles of different or of the same type.

One or more parameters characterizing the operating process, the operating situation, the context, the position, a driving situation, a boundary condition, etc., in which the (respective, first and / or second) vehicle 10, 11, 12, ... 20, 21, 22, ... is operated can be determined. A specific occupant and/or a group of occupants can be identified, recognized or estimated. The behavior of the inmate or an inmate from a specific group of people, in particular with regard to frequent and/or repetitive inmate errors by the inmate or the inmate from the specific group of people, can be evaluated. One or more of the determined information can be provided for the detection and/or the prediction of the one or more occupant errors (e.g. in a prediction algorithm).

It can be determined, in particular predicted, that the occupant (possibly in the near future) is making an occupant error, in particular an occupant error of a certain type, and/or will make it with at least a certain degree of probability or is already at least partially in the process of making an occupant error close. If necessary, it can also be recognized that there is no occupant error, in particular no occupant error of a certain type, is made. This information can also be taken into account when determining the measures and/or during the general operation of the vehicle 10, 11, 12, ... 20, 21, 22, ....

As part of the method 100, a measure of a probability and/or a measure of a consequence of an occupant error that has already been made, is currently occurring or has been predicted can be determined and taken into account.

Depending on the information identified, one or more measures can be identified. A measure can be determined that is intended to prevent a (specific, expected) occupant error and/or to avoid or reduce a consequence of the occupant error. Alternatively or additionally, a measure for occupant information and/or for changing the behavior of the vehicle (e.g. one or more specific functionalities or those affected by the operating error) 10, 11, 12, 20, 21, 22 can be determined. Two or more different measures are preferably determined.

A prerequisite can (each) be determined or adjusted for one or more measures. A precondition can identify a specific trigger in which a specific measure, in particular one of several measures, is selected, parameterized, initiated and/or executed. The precondition can depend on a parameter of the operating process, the operating situation, the context, the position, the driving situation, a boundary condition in which the vehicle 10, 11, 12, ... 20, 21, 22, ... is operated, and/or dependent on any other data.

For example, the at least one precondition can be linked to an action and/or an operating action by the occupant. The precondition can also be linked to a specific pattern from the data mentioned in this document. Alternatively or additionally, a precondition can be linked to a certain level of probability and/or to a consequence of an occupant error.

For example, measures can be linked to respective preconditions or measures and preconditions can be assigned to one another, e.g. linked. The measures and/or preconditions can, for example, be initiated before their (possible) execution.

In an example that can be combined with all the features of this document, at least one measure and/or at least one precondition can be dependent on one or more settings of the vehicle 10, 11, 12, ... 20, 21, 22, ... and/or on one Selection of the occupant can be determined.

For example, the precondition or a parameter of the precondition can be selectable (e.g. by the occupant himself). For example, the execution of (certain) measures can be linked to (certain), in particular selectable, preconditions by means of a configuration or settings.

One or more measures can be initiated, each of which is linked to one or more preconditions. Different measures with different preconditions can preferably be initiated for several (currently probable or impending) variants of an occupant error.

For example, the measures can be determined in the backend BE and transmitted to the respective vehicle 10, 11, 12, ... 20, 21, 22, .... This has the advantage that relatively complex data aggregation DV and/or prediction can also be determined in the backend BE. This does not necessarily have to be provided in the vehicles.
For example, a list of instructions that is characteristic of carrying out and/or triggering certain measures in the event of certain events is determined and initiated on the vehicle 10, 11, 12, 20, 21, 22.

Depending on whether the occupant error and, if applicable, which variant of the occupant error is to be expected, a measure can then be selected and carried out (in real time, with low resource consumption). This can result in a decision regarding a measure being delayed. The number of probable occupant errors and/or measures can, for example, be gradually reduced to particularly likely variants. This enables improved, precise and effective functionality.

As part of the method 100, a parameter of a further course of the operating process, the operating situation, the context, the position, the movement profile, a driving situation, a boundary condition in which the vehicle 10, 11, 12, ... 20, 21, 22 , ... is served, and/or further data is determined. One or more preconditions can be checked (e.g. continuously, cyclically).

As part of the method 100, a measure can be initiated and/or carried out when a (certain, expected) occupant error is detected and/or when the at least one prior condition is met and/or when a certain level of probability and/or consequence of a (possible, expected) occupant error increases.

As part of the method 100, one or more different measures can be carried out with respect to the second vehicle 20, 21, 22, depending on the information determined and/or depending on the progress of the occupant error.

The method 100 described in this document provides, so to speak, individually tailored support for (e.g. human, particularly typical for certain people or groups of people) occupant errors.

Furthermore, the performance of a vehicle (also to be understood as: vehicle functionality or vehicle system) 10, 11, 12, ... 20, 21, 22, ... can be increased. Furthermore, the subjective experience when using the vehicle can be improved.

The system can also be designed in such a way that the detection of occupant errors and the reaction to occupant errors can be continuously improved. The measures described can reduce the consequences of occupant errors. A correspondingly set-up prediction algorithm can be used to recognize the consequences of (certain) occupant errors.

The measures described make it possible to design a vehicle 10, 11, 12, ... 20, 21, 22, ... with increased complexity, since possible occupant errors can be recognized at an early stage, their consequences can be largely defused or, if necessary, compensated for .

The system can help an inmate make fewer inmate errors or cause fewer consequences. Furthermore, the system can also adapt to the occupant and/or to the one or more occupant errors specific to the occupant.

In particular, the invention also includes the case in which the second vehicle is the first vehicle (at a later point in time) or is viewed as such. Alternatively or additionally, the second occupant may be the first occupant (at a later point in time). Particularly in such a case, person-specific occupant errors, in particular those that are repeated particularly frequently by the occupant, can be handled according to the principles disclosed in this document.

The present invention is not limited to the exemplary embodiments shown. In particular, it should be noted that the description and the figures are only intended to illustrate the principle of the proposed methods, devices and systems by way of example.

Claims (17)

Method (100) for supporting an occupant of a vehicle (10, 11, 12, 20, 21, 22) when operating the vehicle (10, 11, 12, 20, 21, 22), comprising: - Determining (101) an occupant error during one or more first operating processes of a first vehicle (10, 11, 12); - Detecting (102) a second operating process of a second vehicle (20, 21, 22); wherein the second operation has at least some degree of similarity to the one or more first operations; and then: - Execute (103) a measure in relation to a possible occupant error during the second operating process. Procedure (100) according to Claim 1 , wherein the measure comprises: - Changing an information output channel in which information is output to an occupant of a vehicle, in particular towards a visual, auditory, haptic and / or kinesthetic information channel. Procedure (100) according to Claim 1 or 2 , wherein the measure comprises: - changing an information input channel that is activated to an occupant of the second vehicle for operating input or information input, in particular is offered for inputting information; and/or - changing the information input channel via which input from the occupant of the vehicle is at least primarily processed, in particular towards voice input, gesture control, touchscreen and/or physically movable control element. Method (100) according to one of the preceding claims, wherein the measure comprises: - changing a parameter of an output and / or an input option in connection with the respective operating process, in particular: - a scope of the output; and/or - type of representation and/or level of detail Output; and/or - position of the output that can be perceived by the occupant. Method (100) according to one of the preceding claims, comprising: - Determining one or more driving situation parameters and/or context parameters in relation to a boundary condition and/or in relation to a context of the occupant error in the one or more first operating processes; and - Recognizing the second operating process and/or executing (103) the measure depending on the one or more driving situation parameters and/or context parameters. Method (100) according to one of the preceding claims, comprising: - Determining at least one consequence parameter in relation to a consequence and/or an effect of the possible occupant error in the second operating process; and - Execute (103) the measure depending on the consequence parameter. Method (100) according to one of the preceding claims, wherein an occupant error indicates an error in operation of an information system and/or communication system, and/or the display operating system of the respective vehicle (10, 11, 12, 20, 21, 22) and/or or when interacting with an information system and/or communication system, and/or the display operating system of the respective vehicle (10, 11, 12, 20, 21, 22). Method (100) according to one of the preceding claims, comprising: - Determining one or more first parameters in relation to the one or more first operations; - Determining one or more second parameters in relation to the second operating process; - Determining a similarity measure based on the one or more first parameters and based on the one or more second parameters; and - Determine, based on the determined similarity measure, whether or not the second operating process has at least a certain similarity measure to the first operating process. Method (100) according to one of the preceding claims, comprising: - Determining first occupant information in relation to a first occupant and/or in relation to a group of people to which the first occupant belongs, through which the occupant error was caused in the one or more first operating processes; and - Recognizing (101) the second operating process and/or executing (103) the measure depending on the first occupant information. Procedure (100) according to Claim 9 , comprising: - determining second occupant information in relation to a second occupant and/or in relation to a group of people to which the second occupant belongs, through whom the second operating process takes place or at least is expected to take place; and - recognizing a similarity between the one or more first operations and the second operation based on the first occupant information and based on the second occupant information. Method (100) according to one of the preceding claims, comprising: - Determining an input from an occupant of the vehicle (10, 11, 12, 20, 21, 22) regarding a measure to be carried out, in particular an association between a specific operating error or an operating error of a certain type and the measure then to be carried out or a type of measure to be carried out Measure; and - Execute (103) a measure dependent on the occupant's input. Method (100) according to one of the preceding claims, wherein the measure is aimed at: - to change an operating logic and/or a parameter of a user interface of the second vehicle (20, 21, 22), in particular temporarily and/or in a passenger-specific manner; and or - the measure comprises a change, in particular temporary and/or occupant-specific, of an operating logic and/or a parameter of the operating interface of the second vehicle (20, 21, 22). Method (100) according to one of the preceding claims, wherein - the measure includes an issue to an occupant of the second vehicle (20, 21, 22); and - the output includes a suggestion for changing a behavior of the vehicle (20, 21, 22), in particular the behavior of an information output device, an information input device and / or a user interface of the second vehicle (20, 21, 22). Method (100) according to one of the preceding claims, comprising: - determining that the one or more first operations have been at least partially undone; and - thereupon, determining that the one or more an occupant error occurred during the first operations. Method (100) according to one of the preceding claims, wherein the method (100) comprises, - Determining data relating to a plurality of first operations; - determining, based on the data, frequency information relating to a frequency of the occupant error in the plurality of first operations; and - Execute (103) the measure depending on the frequency information determined. Computer program product, comprising a computer program, wherein the computer program product is designed, when executed on at least one computing unit, one or more steps of the method (100) according to at least one of Claims 1 until 15 to carry out. System for assisting an occupant of a vehicle in operating a vehicle (10, 11, 12, 20, 21, 22); where the system is set up: - to determine (101) an occupant error during one or more first operating processes of a first vehicle (10, 11, 12); and - to recognize (102) a second operating process of a second vehicle (20, 21, 22); wherein the second operation has at least some degree of similarity to the one or more first operations; and then: - carry out a measure in relation to a possible occupant error during the second operating process (103).

Images