DE102019205892A1 - Method for operating a motor vehicle and motor vehicle that is designed to carry out such a method - Google Patents

Abstract

The invention relates to a method for operating a motor vehicle (10) and a motor vehicle (10) which is designed to carry out such a method. The motor vehicle (10) comprises at least one vehicle system (12) which is designed to control the motor vehicle (10) at least partially autonomously, with a predefined driving behavior mode (16) being activated in the vehicle system (12), which comprises at least one control parameter (17) which defines driving behavior in a predetermined geographical area (20) and which is taken into account when the motor vehicle (10) is at least partially autonomous. A current geographical position (11c) of the motor vehicle (10) is taken into account, it is determined in which predetermined geographical area (20) this current geographical position (11c) is located, and it is determined which predetermined driving behavior mode (16) of this determined geographical area (20 ) assigned. The determined driving behavior mode (16) is activated in the vehicle system (12), whereupon the motor vehicle (10) is controlled at least partially autonomously, taking into account the at least one control parameter (17) of the activated driving behavior mode (16).

Description

The invention relates to a method for operating such a motor vehicle which comprises at least one vehicle system which is designed to control the motor vehicle at least partially autonomously. The invention also relates to a motor vehicle with a vehicle system, the motor vehicle being designed to carry out said method.

A prerequisite for a fully autonomous driving function for a motor vehicle that can be used worldwide is that the driving function imitates the driving behavior of an average human driver during a fully autonomous journey. It is therefore necessary for the driving behavior of the fully autonomous motor vehicle to be based on local conditions and driving styles. For example, the case may arise that a maximum speed indicated on a road sign before a road curve is to be taken more seriously in one country than is the case in another country. This means that when a motor vehicle travels through the curve at a speed that corresponds to the specified maximum speed, the motor vehicle experiences higher lateral forces in the first-mentioned state than in the second-mentioned state. A human driver learns such regional characteristics quickly and can adapt to them. The next time he is in the first-mentioned state, the human driver will therefore, for example, drive more carefully into a curve and, for example, drive there slower than the maximum speed specified there. In addition, the human driver will in particular avoid driving there at a higher speed than the maximum speed displayed allows. There are also various differences in driving styles and in the interpretation of traffic signs and traffic rules that a driver usually learns as part of his training in a driving school in the relevant state or district. Because driving behavior in large metropolitan areas, such as Paris or Beijing, differs significantly from driving behavior in more rural regions, such as in Rwanda or on a serpentine road in the Alps. It can therefore be assumed that there is no globally uniform model on the basis of which fully autonomous driving should be made possible everywhere.

Furthermore, it is assumed that a motor vehicle designed for fully autonomous driving no longer has a steering wheel or operating elements for other intervention options for a person during fully autonomous driving. This motor vehicle must therefore be able to drive the motor vehicle completely independently, that is to say fully autonomously. However, motor vehicles that have, for example, driver assistance systems that are designed at least for partially autonomous driving must be able to control the motor vehicle in such a way that the partially autonomously driving motor vehicle does not behave abnormally, which for example causes accidents with other motor vehicles that are driven by manually controlled by human drivers. Such atypical behavior occurs particularly frequently in situations in which a manual driver would not brake his motor vehicle, but the at least partially autonomously controlled motor vehicle would, however, perform an emergency stop, for example due to traffic rules or decision conflicts.

In the DE 10 2012 016 802 A1 describes a method for controlling a vehicle system designed for the autonomous operation of a motor vehicle. Here, a current position of the motor vehicle is recorded and, as a function of this position information, the admissibility of using a driver assistance system is checked. Depending on the position of the motor vehicle, for example, a maximum speed up to which autonomous driving is permitted, a spatial area in which autonomous driving is permitted, and limit values with regard to other parameters can be set.

The DE 10 2014 217 389 A1 shows a system for autonomous driving of a vehicle. In this case, a message is transmitted to the vehicle, whereupon a driving state of the vehicle is determined based on whether the message was received and / or a content of the message was identified. Driving instructions are determined at least partially based on the driving state.

The DE 10 2016 120 508 A1 FIG. 10 shows a system, method, and apparatus for predicting driver intent and future motion of a proximal vehicle that is either an automated vehicle or a human-driven vehicle. The aim here is that autonomously driving vehicles can predict the movement of the surrounding vehicles, even if they are being driven by a person. This takes place on the basis of a detected movement of the other vehicles, a body language of drivers of the other vehicles, a travel direction display of the vehicles or their absence and wireless communication, for example between individual vehicles.

It is the object of the invention to provide a solution which is taken into account when a motor vehicle is at least partially autonomous which regional driving behavior peculiarities exist.

This object is achieved by the subjects of the independent claims. Advantageous refinements with expedient and non-trivial developments of the invention are specified in the dependent claims, the following description and the figures.

A motor vehicle is operated with the method according to the invention. The motor vehicle comprises at least one vehicle system, this vehicle system being, for example, a central vehicle controller or a control device of a driver assistance system of the motor vehicle, each of which is designed to control the motor vehicle with regard to longitudinal guidance and / or lateral guidance. The at least one vehicle system is designed to control the motor vehicle at least partially autonomously, a predetermined driving behavior mode being activated in the vehicle system. The predefined driving behavior mode comprises at least one control parameter which defines driving behavior in a predefined geographical area and is taken into account in the at least partially autonomous control of the motor vehicle. The geographic area is, for example, a certain state or a certain city. In the context of the invention, driving behavior is understood to mean a behavior of a driver mimicked by the vehicle system when driving the motor vehicle manually in road traffic in the geographical area. Here, a set of technical properties of the motor vehicle that occur while the driver is driving is understood as driving behavior. The driving behavior can specify, for example, whether speed limits, for example a typical under-speed and / or over-speed of the driver, on an expressway, country road and / or in inner-city traffic. The driving behavior can thus be quantified with the aid of the control parameter, which includes, for example, that the average driver typically always drives 10 kilometers per hour slower on the expressway than the maximum speed specified on this expressway in the specific area, for example, there the expressway is more curvy than a typical expressway, on which the specified maximum speed is based. In this example, the control parameter consequently includes the information about the speed below 10 kilometers per hour on expressways, which reflects typical driving behavior. This information comprised by the activation parameter is then taken into account when the motor vehicle is actually activated during its at least partially autonomous drive, which is carried out, for example, by an autopilot. The driving behavior mode thus includes that the autopilot, which in this example represents the vehicle system of the motor vehicle, always falls below the locally specified maximum speed by 10 kilometers per hour and thereby adapts to the typical driving behavior in the specified geographical area.

The method according to the invention comprises the following steps: First, a current geographical position of the motor vehicle is recorded. This acquisition takes place, for example, by means of a acquisition device of the motor vehicle to which data from a global navigation satellite system (GNSS) is provided, for example. The specified geographical area in which the motor vehicle is currently located is then determined on the basis of the detected geographical position. This determination can take place, for example, in an evaluation device of the vehicle system or in a control device of the motor vehicle coupled to the vehicle system. In this method step it can be determined, for example, that the motor vehicle is located in a certain country, for example Portugal, based on its current geographical position. Thereupon it is now determined, for example also by the evaluation unit, which predefined driving behavior mode of several predefined position-dependent driving behavior modes is assigned to the determined geographical area. It is thus determined which predefined driving behavior mode with its respective at least one control parameter defines the driving behavior in the determined geographical area in which the motor vehicle is located. For example, a respective driving behavior mode can be differentiated for individual states, such as Portugal, Germany and France, in addition to the state-based assignment of the geographical area, the area of the state can also be subdivided into individual urban areas, for example. For example, a driving behavior mode for large cities such as Paris or Berlin can be specified separately from the driving behavior modes for France or Germany.

Thereupon it is now checked whether the determined driving behavior mode is already activated in the vehicle system. If the determined driving behavior mode is currently not activated in the vehicle system, the already activated driving behavior mode is deactivated and the determined driving behavior mode is activated. In the vehicle system, only one driving behavior mode is activated at any one time. An interaction of several driving behavior modes, such as the driving behavior mode for France and the driving behavior mode for Paris within, for example, the city of Paris is therefore not provided, but a separate driving behavior mode is specified for Paris. If, for example, the motor vehicle is traveling in a border area between two specified geographical areas, because it has driven into the urban area of Paris from an expressway in rural France, for example, it may be that the driving behavior mode for France is still activated in the motor vehicle and not the specified driving behavior mode for the city of Paris. If this is the case, after it has been determined that the motor vehicle is actually in Paris, the previously activated driving behavior mode for France is deactivated and the determined driving behavior mode is activated, i.e. the driving behavior mode for the city of Paris.

In a next step, the at least partially autonomous control of the motor vehicle takes place, taking into account the at least one control parameter of the currently activated driving behavior mode. In the example mentioned, the at least partially autonomous control of the motor vehicle will therefore take place after activating the determined driving behavior mode for the city of Paris, taking into account the control parameter of the driving behavior mode for Paris. The method described is based on the knowledge that within a city, for example, a different typical driving behavior is observed by drivers of motor vehicles than is observed, for example, in a rural area and thus in other parts of the state in which the city is located. So that a motor vehicle driving fully autonomously, for example, does not represent an obstacle or nuisance for other road users who, for example, still manually control their motor vehicle, the driving behavior mode for the urban area can therefore include at least one control parameter other than the driving behavior mode for the rural area of the same state.

With the aid of the driving behavior mode assigned to the current geographical area, it can thus be achieved that the motor vehicle behaves in its at least partially autonomous driving in exactly the same way as an average human driver actually behaves in the current geographical area. In this way, for example, the probability and frequency of traffic complications due to unexpected and / or atypical behavior of the, for example, partially or fully autonomous driving motor vehicle is reduced, since this adapts to the local driving conditions. The method according to the invention thus allows the local driving behavior to be taken into account and also ensures that the suitable driving behavior mode is always activated at a specific point in time. The method according to the invention thus has the advantage that regional driving behavior peculiarities are taken into account when the motor vehicle is actuated at least partially autonomously.

The invention also includes embodiments that result in additional advantages.

In an advantageous embodiment of the invention, it is provided that the at least one control parameter is determined from data of at least one of the following types of data: traffic control data that characterize at least one traffic rule currently applicable in the geographic area, driving behavior data that is relevant for a driver of a vehicle in the geographic area Characterize typical driving behavior in the area, road user behavior data that characterize behavior that is typical for at least one road user in the geographical area, interpretation data that characterize an interpretation rule for at least one traffic symbol in the geographical area and / or vehicle identification data that characterize the motor vehicle.

The at least one control parameter on which the driving behavior mode is based is thus determined, for example, from data that describe the applicable traffic rules in the geographic area. These traffic regulation data include, for example, information on left-hand or right-hand traffic that is prescribed in the geographical area. In addition, this data includes legal regulations with regard to traffic management in the geographic area. The driving behavior data in turn includes information on how the driver of any vehicle typically behaves in the geographic area. The driving behavior data can characterize the respective typical driving behavior of several drivers from a total of several respective vehicles in the geographic area. The driving behavior data therefore include, for example, how likely it is that the driver will stop in the geographical area for a pedestrian in front of a traffic light that is green for the vehicle and / or a zebra crossing and / or by what percentage, for example, within an urban area, slower and / or drives faster than the specified maximum speed dictates. Stopping or at least reducing the speed of the vehicle by the driver for a pedestrian despite a green traffic light switch for the vehicle can result in certain geographical areas as a reaction to impatiently waiting pedestrians, especially in urban areas, and can take into account at least one control parameter will. The road user behavior data, on the other hand, describe typical behavior of others Road users in the geographical area, such as how much distance these other road users keep from the vehicle of the driver providing the driving behavior data or how other road users react in a dangerous situation, for example if a child walks on the street. The interpretation data include information on how traffic signs, lane markings and thus different traffic symbols are recorded in the geographical area and how they influence driving behavior. Here, for example, interpretations of traffic rules not written down in a set of rules but already learned in the driving school in the geographical area are taken into account, which are reflected in the actual driving behavior of drivers in the geographical area. The vehicle characteristic data include, for example, a model type of the motor vehicle, such as its length and width, as well as characteristic data with regard to the vehicle system and / or, for example, a drive of the motor vehicle. This information is particularly relevant, for example, with regard to driving on a narrow street or in a parking lot.

The at least one control parameter is thus based on the information that ultimately contributes to determining the driving behavior in the geographical area. The at least one control parameter can be determined by means of a suitable calculation and weighting of the information that can be taken from the individual data mentioned. In order to determine the control parameter, all available data with regard to the behavior of manual drivers, other road users, the existing traffic rules, the interpretation of traffic symbols and information about the motor vehicle itself are therefore available, so that a reliable control parameter that describes the driving behavior typical for the geographical area, can be determined.

A further particularly advantageous embodiment of the invention deals with the question of which subsequent steps are used to define the geographical area for the respective driving behavior mode. First, vehicle data is provided from at least one vehicle which is operated by the respective driver and which records the vehicle data with at least one detection device of the vehicle. The at least one detection device is, for example, a sensor device of the vehicle, such as a camera system, a radar device, a laser scanner and / or an infrared camera system. As an alternative or in addition to this, the detection device can be, for example, a temperature sensor, a speed measurement sensor and / or a navigation system of the motor vehicle that provides position data of the vehicle. The vehicle data are stored, for example, in a memory unit of the respective vehicle and, for example, after a journey, are transmitted to an external computing device via a communication connection. This computing device is designed, for example, as a server device. The respective vehicle data describe at least one driving maneuver of the at least one vehicle and additionally include location data that characterize a detection location of the vehicle data. For the respective vehicle data, a respective detection location is therefore additionally stored in the form of the location data, so that conclusions can be drawn as to where the respective vehicle data were recorded by the detection device. If the vehicle data is, for example, speed data that indicate the speed at which the vehicle was driven, the corresponding data contain, in addition to the respective speed values, the respective location data that indicate where the respective speed value was recorded. The computing device can have a total of vehicle data from several vehicles, which were recorded and stored, for example, in several driving maneuvers during which the respective vehicle was controlled by at least one respective driver.

In a next step, the computing device determines the driving behavior data and / or the road user behavior data. The driving behavior data and / or the road user behavior data are determined by applying a method of machine learning to the vehicle data provided. These driving behavior data then contain, for example, information about how many percent or how many kilometers per hour the driver drove too slowly and / or too fast on a road of a certain type of road, such as an expressway, which is based on the vehicle data and the location data assigned to the vehicle data can be determined. This is because the vehicle data include, for example, the recorded speed of the vehicle and image data recorded by the vehicle's camera system from a road sign with the specified maximum speed on the road. It is thus possible to obtain data that characterize how the at least one driver of the vehicle behaves during his journey. Additionally or alternatively, data can be obtained that describe how at least one road user behaves. The information about the other road users can, for example, by means of data recorded with the camera system, the laser scanner, the radar device and / or the infrared camera system are also provided as vehicle data from the at least one vehicle.

In a next step, at least one data pattern is recognized in the determined driving behavior data and / or the determined traffic user behavior data by applying a method of machine learning to the determined driving behavior data and / or the determined traffic user behavior data. A machine learning method is used both to determine the driving behavior data and / or the road user behavior data and to identify at least one data pattern in the driving behavior data and / or the traffic user behavior data determined. This is an artificial intelligence method that is carried out, for example, by using a respective artificial neural network. It is therefore provided that, for example, an artificial neural network is applied to the vehicle data provided to the computing device.

An artificial neural network is a network of artificial neurons. In neuroinformatics, artificial neural networks are used as artificial intelligence because they are suitable for evaluating this data, for example without detailed knowledge of a mode of operation or a communication relationship between individual subsets of data. An artificial neural network is an independently learning computer program that can recognize complex regularities and relationships in large amounts of data in a relatively short time. For this purpose, a neural network is based on an abstract model of interconnected neutrons, so that an artificial neural network is based at least to a certain extent on the structure of a biological brain. In simplified terms, the structure and functioning of an artificial neural network can be described with a network of neurons that each receive information from outside or from other neurons and, in modified form, forward it to other neurons or output it as an end result. Here, for example, positive or negative weightings can be made, which represent an exciting or inhibiting influence of the neurons on one another. The knowledge and thus the artificial intelligence of an artificial neural network is ultimately stored in the connections between the individual neurons and their weightings. During training of the neural network, these weightings of the connections change depending on the learning rules applied and the results achieved.

In other words, by applying the artificial neural network to the vehicle data, it can be achieved that the driving behavior data and / or the road user behavior data can be determined from this vehicle data within a short period of time. In addition, conspicuous data packets, the so-called data patterns, can be identified in the determined driving behavior data and / or the road user behavior data. In a simplified example, the differences between the permitted driving speed and the actual maximum speed determined in the determination of the driving behavior data can be plotted as a function of a number of vehicles in which such a difference was observed. A function graph resulting from this has, for example, several maxima and minima. The drivers, whose data are assigned to a respective range of the maxima of such a function graph, consequently show a similar behavior with regard to a certain underspeed, for example due to a winding road. For example, such a maximum occurs when the maximum permissible speed is typically not reached by 10 kilometers per hour.

In a next step, the definition of an extent of the respective geographical area takes place, taking into account the at least one recognized data pattern and the location data comprised by the vehicle data. If, for example, it is now found that all the vehicle data that suggest that the maximum speed was not exceeded by 10 kilometers per hour was found in a certain limited area, for example on various roads in a mountainous region, the extent of this mountainous region can be defined as a geographical area . By using artificial intelligence methods, conclusions can be drawn about relationships or similar behavior of individual vehicle data subsets, driving behavior data subsets and / or road user behavior data subsets, so-called clusters. On the basis of these similarities, a geographical area can ultimately be determined in which drivers behave similarly. Ultimately, for example, conclusions are drawn about coordinates that describe the geographic area. This makes it possible, using vehicle data provided by, for example, several vehicles, ultimately by using the methods of machine learning, in particular in a short period of time and thoroughly, to distinguish between individual geographical areas that differ in the driving behavior of drivers of vehicles and / or drivers that are common there differentiate between other road users. Ultimately, this helps that a suitable geographical subdivision of the world traveled by vehicles is made possible so that ultimately, based on the recorded extent of the respective geographical areas, it is recognized how many and which specific driving behavior modes must be determined and provided for motor vehicles in order to enable at least partially autonomous driving local characteristics in driving behavior can be taken into account.

A particularly advantageous embodiment of the invention also provides that setting the at least one control parameter of the predetermined driving behavior mode for the respective geographical area comprises the following steps: First, a real behavior model is defined for the respective geographical area, taking into account the determined driving behavior data and / or road user behavior data, which are assigned to the respective specified geographical area. Ultimately, a model is thus determined that describes how a typical driver of a vehicle behaves in the geographic area. Using this model, for example, the average driving behavior of a human driver can be simulated for the respective geographic area. The real behavior model is also established, for example, in the external computing device and preferably not within the motor vehicle itself.

The computing device is also provided with the traffic regulation data, interpretation data and / or vehicle identification data that are assigned to the respective defined geographical area or the respective motor vehicle. An ideal behavior model is then determined for the at least one motor vehicle in the defined respective geographical area, taking into account the aforementioned provided traffic control data, interpretation data or vehicle characteristic data. The information about the traffic rules applicable in the geographical area, the interpretation regulations that prevail in the geographical area and the vehicle characteristics that describe the motor vehicle in which the respective driving behavior mode is to be activated are taken into account in order to achieve an ideal and thus to determine the driving behavior model corresponding to the legally prescribed behavior, which is referred to as the ideal behavior model.

The at least one control parameter for the respective geographical area is then determined from the real behavior model and ideal behavior model established for the respective defined geographical area by applying a predetermined weighting criterion. All the data or at least parts of the data that have already been mentioned above as data from which the at least one control parameter is determined are therefore used to determine the control parameter.

With the aid of an evaluation system that is provided by the weighting criterion, a comparison is made between the real behavior model and the ideal behavior model, from which the driving behavior mode is ultimately derived. For example, driving maneuvers that do not comply with the applicable traffic rules and that are stored in the real behavior model, for example, are not transferred to the driving behavior mode, but a suitable comparison of the actually observed behavior with the legally prescribed behavior contained in the ideal behavior model is carried out. This makes it possible for a driving behavior dependent on the defined geographical area to be quantified in such a way that the at least one control parameter is provided, on the basis of which the vehicle system can ultimately control the motor vehicle at least partially autonomously. The choice of the weighting criterion consequently enables a driving behavior that is as realistic as possible, but still sufficiently compliant with the rules, to be implemented in the at least partially autonomous motor vehicle using the driving behavior mode. The irritation and hindrance of manually driving road users by a motor vehicle that is controlled according to the driving behavior mode is therefore particularly small, since the driving behavior mode is based directly on the actual driving behavior in the specific geographical area.

In a further embodiment it is provided that the weighting criterion is determined by a simulation and / or a shadow mode function. The evaluation criterion is thus determined with the help of a learning system. For example, when simulating a recorded driving maneuver on the basis of the vehicle data, by simulating the driving behavior in the corresponding situation according to the real behavior model and the ideal behavior model, it can be determined which of the model is weighted to a desired functional quality, i.e. a desired behavior during the driving maneuver , to reach. When determining the weighting criterion with the aid of the shadow mode function, a first version of the weighting criterion is provided in the motor vehicle, the motor vehicle itself driving without taking into account the weighting criterion or any driving behavior mode. However, an evaluation unit of the motor vehicle determines, for example, for each manually driven driving maneuver, an expected behavior of the motor vehicle when the driving behavior mode is activated. The driving behavior mode is therefore only activated in the background, so that the weighting criterion can be adapted by comparing the driving behavior results of this driving behavior mode, which works quasi in the shade, with the actual driving behavior of the driver. The weighting criterion is set in such a way that an optimal correspondence between the simulated activation using the driving behavior mode and the actual driving behavior of the manual driver can be achieved. The mode referred to here as the shadow mode function is often also referred to as shadow mode. The suitable weighting criterion is therefore determined particularly easily by the simulation and / or the shadow mode function.

Another embodiment of the invention provides that when determining a route from the current geographical position to a predetermined target position of the motor vehicle, a check is made as to whether the motor vehicle travels through several predetermined geographical areas while driving along the particular route. For example, as part of the route determination, it is already established whether the motor vehicle will be in more than one predetermined geographical area on this route. If several predetermined geographical areas are traveled through, the driving behavior modes which are assigned to the respective predetermined geographical area are downloaded from the external device via a communication link of the motor vehicle. It is thus ensured that all driving behavior modes required for this route are already available in the motor vehicle at the start of the journey, so that, depending on the current position of the motor vehicle, which is recorded, for example, at predetermined time intervals, it is possible to switch between the individual already downloaded driving behavior modes. It is possible, for example, that when driving from one city to another, both the respective driving behavior modes of the starting city and the destination city as well as corresponding driving behavior modes for an overland journey between the two cities are downloaded. It is consequently not necessary that all driving behavior modes available for an entire country, continent or even the whole world are always stored in the vehicle system, in a control device coupled to the vehicle system or in a storage device of the motor vehicle coupled to the vehicle system. Rather, only the driving behavior modes can always be stored in the motor vehicle, for example by temporarily storing them in the storage device that the motor vehicle actually needs during the planned route. Particularly with regard to fully autonomous motor vehicles, it is advantageous that the complete route is already known when the journey begins, so that, for example, regardless of a regionally available or accessible mobile data network, it is ensured that the vehicle system can access all the driving behavior modes required for this route as soon as they are activated are. This makes it possible to reliably use the method for controlling the motor vehicle even in remote regions which, for example, do not have a comprehensive coverage by a mobile data network.

In this context, it is provided in an advantageous embodiment that a transition from the previous driving behavior mode to the determined driving behavior mode occurs when the motor vehicle is traveling over a predetermined transition route. There is therefore not necessarily an abrupt transition from the previous driving behavior mode to the current driving behavior mode specified on the basis of the current position of the motor vehicle. Rather, it can be provided that, for example, a transition area of, for example, 5 kilometers in length is specified between two geographically adjacent geographical areas. If it is detected that the motor vehicle is in this transition area, a continuous transition from the previous driving behavior mode to the new driving behavior mode in the adjoining and soon to be reached geographical area is activated for the 5 km transition route through the transition area. This prevents the occupants of the motor vehicle, which is driving fully autonomously, for example, from being surprised by a suddenly abrupt change in driving style. Rather, the occupants of the motor vehicle can continuously get used to the changed driving behavior of the motor vehicle and adjust to it.

Alternatively, an advantageous embodiment of the invention provides that the determined driving behavior mode is downloaded from an external device via a communication link to the motor vehicle, it being determined that the motor vehicle is located in the geographical area assigned to the determined driving behavior mode. In this alternative embodiment, the driving behavior mode is always downloaded into the motor vehicle first when it is determined that the motor vehicle is located in the associated geographic area. The prerequisite for this is that a communication connection with the external device in which the respective driving behavior modes are stored can be established and maintained. This enables the advantage that the large amounts of data for the respective driving behavior modes do not have to be stored in the motor vehicle itself at all times, since the corresponding data are only downloaded when they are actually needed by the vehicle system. In this way, the requirement of the method for a storage capacity of the motor vehicle is kept as low as possible.

In a particularly advantageous embodiment of the invention it is provided that the geographical area comprises at least one of the following areas: a state, a member state, a district and / or a settlement. The geographical area thus comprises a relatively large area, especially if it comprises a single state, that is to say a single country on earth. As an alternative or in addition to this, in the case of different driving behavior for the individual member states, such as federal states, federal states, at least partially autonomous regions or other political sub-states, a separate driving behavior mode can be provided in each case. It may also be necessary that a specific driving behavior mode is provided for a specific district, such as a district, or a metropolitan area, such as a large city, a small town or a village, i.e. for a settlement, since the typical Driving behavior in these small geographical areas differs markedly from the surrounding areas. Depending on how locally differentiated the driving behavior is, different types of geographical areas are possible, so that a sufficiently differentiated division into geographical areas can always take place.

According to the invention, a motor vehicle with a vehicle system is also provided, the vehicle system being designed to control the motor vehicle at least partially autonomously. The vehicle system or a control device coupled to the vehicle system is designed to carry out a method as described above. The preferred embodiments presented in connection with the method according to the invention and their advantages apply accordingly, if applicable, to the motor vehicle according to the invention. For this reason, the corresponding developments of the motor vehicle according to the invention are not described again here. The vehicle system can be a central control device of the motor vehicle, for example a central vehicle controller. However, the vehicle system can also be a control device of a driver assistance system. In this case, the method is partly carried out by the control device coupled to the vehicle system. The control device is then designed to control, for example, a longitudinal control and / or lateral control of the motor vehicle, taking into account the control commands from the control device of the driver assistance system.

The invention also includes the vehicle system and the control device for the motor vehicle that is coupled to the vehicle system. The vehicle system or the control device each have a processor device which is set up to carry out an embodiment of the method according to the invention. For this purpose, the processor device can have at least one microprocessor and / or one microcontroller. Furthermore, the processor device can have program code which is set up to carry out an embodiment of the method according to the invention when it is executed by the processor device. The program code can be stored in a data memory of the processor device.

The invention also comprises the combinations of the features of the embodiments described.

• 1 eine schematische Darstellung von drei Kraftfahrzeugen in verschiedenen geografischen Gebieten,
• 2 in schematischer Darstellung einen Signalflussgraph für ein Festlegen eines geografischen Gebiets;
• 3 in schematischer Darstellung einen Signalflussgraph für ein Festlegen von zumindest einem Ansteuerparameter für einen Fahrverhaltensmodus für ein bestimmtes geografisches Gebiet; und
• 4 in schematischer Darstellung einen Signalflussgraph für ein Verfahren zum Betreiben eines Kraftfahrzeugs.

Exemplary embodiments of the invention are described below. This shows:

• 1 a schematic representation of three motor vehicles in different geographic areas,
• 2 a schematic representation of a signal flow graph for defining a geographic area;
• 3 a schematic representation of a signal flow graph for establishing at least one control parameter for a driving behavior mode for a specific geographic area; and
• 4th a schematic representation of a signal flow graph for a method for operating a motor vehicle.

The exemplary embodiments explained below are preferred embodiments of the invention. In the exemplary embodiments, the described components of the embodiment each represent individual features of the invention that are to be considered independently of one another, which also develop the invention independently of one another and are thus also to be regarded as part of the invention individually or in a combination other than the one shown. Furthermore, the described embodiments can also be supplemented by further features of the invention already described.

In the figures, functionally identical elements are each provided with the same reference symbols.

In 1 is a plan view of a landscape sketched in the three motor vehicles 10 used as motor vehicles 10a , 10b and 10c be designated, are sketched. The motor vehicles 10a , 10b and 10c each comprise a vehicle system 12 , at least for partially autonomous control of the respective motor vehicle 10a , 10b , 10c is designed. The motor vehicles 10a , 10b and 10c driving on a road 14th , being the street 14th leads through two states, which by means of a state border 22nd are separated from each other. The car 10a is located in a first state, that is, in a geographical area 20th that as a geographic area 20a is referred to, whereas the motor vehicle 10b in the second state behind the state border 22nd in the direction of travel and in a geographical area assigned to the second state 20b moves. The third motor vehicle 10c located in a city that serves as a geographic area 20c referred to as. A city limit 24 determines the boundary between this geographical area 20c and the geographic area surrounding the city 20b of the second state.

In the respective vehicle system 12 is a predetermined driving behavior mode 16 (shown with the reference number 16 in 3 ) activated, the at least one control parameter 17th (shown with the reference number 17th in 3 ) includes. The control parameter 17th specifies driving behavior in the respective given geographic area 20a , 20b , 20c fixed and is at least partially autonomous driving of the motor vehicle 10a , 10b , 10c considered. A change between such driving behavior modes 16 can, for example, when reaching the national border 22nd take place, that is, for example, whenever the motor vehicle 10a the state border 22nd reached and then, as here, by the motor vehicle 10b outlined, in the second state, the geographical area 20b is assigned, drives. Alternatively, the transition between the two mentioned driving behavior modes 16 take place fluently, over a predetermined transition route 15th away, as in 1 using dashed lines that form a section of the road 14th include is outlined. Within this transition route 15th can for example continuously from the at least one control parameter 17th of the geographical area 20a assigned driving behavior mode 16 in the geographical area 20b assigned driving behavior mode 16 change.

In 2 individual procedural steps are outlined that take place around the geographic area 20c to be determined. Defining the other geographical areas 20a and 20b takes place in the same way. In a first step S1 are a computing device vehicle data 30th provided by at least one vehicle. This vehicle is operated by a driver and the vehicle data 30th are detected with at least one detection device of the vehicle, such as a camera system, a radar device, a speed sensor and / or a navigation system. The respective vehicle data 30th describe at least one driving maneuver of the at least one vehicle. For example, describe the vehicle data 30th the speed at which the vehicle is on the road 14th has driven. In addition, include the vehicle data 30th Location data 31 , which is a recording location of the vehicle data 30th characterize. It is therefore known for each speed value where this speed value was detected by the corresponding detection device of the vehicle, that is to say in this example by the speed measurement sensor of the vehicle. The vehicle data 30th can, for example, be provided by a fleet of test vehicles, but also by any vehicles controlled by manual drivers whose drivers have agreed to provide their data for evaluation and optimization purposes.

In a next step S2 are made by applying a method of machine learning, for example by applying an artificial neural network, to the vehicle data provided 30th , Driving behavior data 40 and / or road user behavior data 42 determined. Under driving behavior data 40 data is understood that characterize the driving behavior typical of the driver of the vehicle. As road user behavior data 42 are data that characterize behavior that is typical for at least one road user. As driving behavior data 40 For example, it is quantified how often the at least one vehicle stops when a waiting person is detected at a traffic light that is green for the vehicle and / or at a zebra crossing, what distance the vehicle maintains from cyclists driving on the side of the road, what speed it is on the road 14th drives at a maximum permitted speed of 30 kilometers per hour and / or which braking acceleration is selected in front of a traffic light that turns red in order to brake the vehicle to a standstill. In general, driving behavior data 40 thus expressing a frequency or probability for a certain driving behavior. With the road user behavior data 42 it is, for example, data that shows the frequency of emergency stops due to pedestrians who, for example, cross the street when a pedestrian traffic light is red 14th run, indicate and / or the speeds of other vehicles in an area of the vehicle data 30th providing vehicle, for example also on the road 14th drive. In general, road user behavior data can be used 42 thus expressing a frequency or probability for a certain driving behavior.

It can thus be determined, for example, with what speed reduction and / or speed increase the driver of the at least one vehicle on the road 14th moves. If necessary, image data provided by the vehicle's camera system, for example, from a road sign indicating the maximum permissible speed on the road may be used 14th displays, and / or taken into account information stored in a navigation system of the vehicle on the maximum permissible speed.

In one step S3 becomes at least one data pattern 50 in the determined driving behavior data 40 or the determined road user behavior data 42 by applying the machine learning method to the determined driving behavior data 40 or the determined road user behavior data 42 recognized. In step S3 it is determined, for example, which number 41 of vehicles which driving behavior data 40 has, that is, it is plotted, for example, what number 41 on vehicles at which reduced speed was driven. In this example, for example, there are two maxima in the in 2 under the crotch S3 function graphs shown clearly as a recognizable data pattern 50 can be understood. Especially when applying artificial neural networks to vehicle data 30th as well as from the vehicle data 30th determined driving behavior data 40 and / or road user behavior data 42 This means that regularities in this large amount of data can ultimately become known. The pattern recognition thus relates to the most common behaviors that are recognized as local maximums in the respective frequency distribution.

In a next step S4 is taking into account the at least one recognized data pattern 50 as well as that of the vehicle data 30th recorded location data 31 an extension of the respective geographic area 20c set. For example, for different geographic coordinates, which in this example are each assigned to an x-value and a y-value, the respective data patterns 50 associated driving behavior data 40 plotted, individual areas are shown that these two maxima of the step S3 determined function graphs can be assigned. Consequently, an actual geographic extent of two cities, each as a geographic area 20c are designated. In these, the driving behavior is clearly different or noticeably different than in the surrounding area of the second state, that is, the geographical area 20b . Individual geographical areas determined by such, depending on various parameters 20a , 20b , 20c the areas in which the driver of the at least one vehicle typically behaves at least in a similar manner can thus be identified, and their respective extent can be determined. In these geographic areas 20a , 20b , 20c it can be respective states as it is for the geographic areas 20a and 20b in this example it is a state, a district and / or a settlement, as is the case in this example for the geographical area 20c is the case, act.

In 3 the next procedural steps are described that are required to set the at least one control parameter 17th the driving behavior mode 16 for the geographic area 20c to be determined. This is done in one step S5 first a real behavior model 60 for the specific geographic area 20c set. This is done taking into account the driving behavior data determined 40 and / or road user behavior data 42 corresponding to the geographical area 20c , which was defined as described above, are assigned. Analogously to the steps now described below, the respective at least one control parameter can also be used 17th for the other geographic areas 20a and 20b to be determined. In a next step S6 are the computing device, by means of which, for example, the in 3 procedural steps described have been carried out, traffic control data 44 , Interpretation data 46 and / or vehicle identification data 48 provided. The traffic regulation data 44 characterize at least one in the geographic area 20c currently applicable traffic regulations. The interpretation data 46 include an interpretation rule for at least one traffic symbol in the geographical area 20c . The vehicle identification data 48 characterize the motor vehicle 10a , 10b , 10c , for example its respective length and width and a respective vehicle model type. In a next step S7 is made from the traffic regulation data provided 44 , Interpretation data 46 and / or vehicle identification data 48 an ideal behavior model 70 for the at least one motor vehicle 10a , 10b , 10c in the specified geographical area 20c set. It is thus determined how the motor vehicle is 10a , 10b , 10c taking into account, for example, the legally prescribed behavior in the geographic area 20c should behave. In contrast, the real behavior model reflects 60 that in the crotch S5 was determined, again, how a real driver is in the geographic area 20c typically behaves. In a next process step S8 now become the real behavior model 60 and the ideal behavior model 70 by applying a predetermined weighting criterion offset against one another in such a way that for the geographic area 20c the at least one control parameter 17th is determined. Consequently, this becomes the driving behavior mode 16 for the geographic area 20c at least with regard to the control parameter 17th for the speed of the motor vehicle 10a , 10b , 10c set. Taking into account data from the various types of vehicle data mentioned above, as well as taking into account the as in 2 described specified geographical areas 20a , 20b , 20c can thus have several control parameters 17th can be determined, so that overall a comprehensive driving behavior mode 16 is provided, the driving behavior in the geographic area 20a , 20b , 20c so close to the real driving behavior of drivers in this geographical area 20a , 20b , 20c as possible. The weighting criterion can be determined by a simulation and / or a shadow mode function. Ultimately, the weighting criterion reflects how compliant with the rules or in what type of individual traffic rules in the respective geographic area 20a , 20b , 20c interpreted, so that ultimately the driving behavior mode 16 for a partially or fully autonomous motor vehicle 10a , 10b , 10c always to a control of the motor vehicle 10a , 10b , 10c leads to a manual driver in the geographic area 20a , 20b , 20c largely corresponds.

In 4th the individual process steps with which the motor vehicle 10a , 10b , 10c is operated. In 4th is this using the example of the motor vehicle 10c out 1 outlined. In one process step S9 a current geographical position is first recorded 11c of the motor vehicle 10c by means of a detection device of the motor vehicle 10c . A corresponding position request can, for example, from the vehicle system 12 to a navigation system of the motor vehicle 10c are transmitted, which then provides the position data for the vehicle system 12 provides. The car 10c is on the street 14th on the outskirts of the city, which is the geographical area 10c assigned. The car 10c is currently near the city limits 24 . In a next step S10 it is determined in which given geographical area 20c the motor vehicle 10c currently is what based on the recorded geographic position 11c is determined. The motor vehicle is in this example 10c as described above, in the geographical area 20c . Shortly before, however, it was outside the city and consequently in the geographical area 20b found. In a next process step S11 it is determined which predetermined driving behavior mode 16 of several predetermined position-dependent driving behavior modes 16 the determined geographical area 20c assigned. In this example, it is the geographic area 20c assigned driving behavior mode 16 as driving behavior mode 16c designated. In a next process step S12 it is checked whether the determined driving behavior mode 16c already in the vehicle system 12 is activated. It is determined here that the previous driving behavior mode is currently still in use 16b that corresponds to the geographical area 20b is assigned in the vehicle system 12 is activated.

In a next step 13a becomes the one in the geographical area 20c required driving behavior mode 16c in the motor vehicle 10c downloaded. A communication link is required for this 19th with an external facility 18th , such as an external server device. This communication connection is set up as soon as it is determined that the motor vehicle is 10c in the determined driving behavior mode 16c assigned geographical area 20c is located. This is just beyond the city limits 24 for the motor vehicle 10c at the recorded geographic position 11c the case. The one so far in the vehicle system 12 saved driving behavior mode 16b can now be deleted, for example, as soon as the current position 11c assigned driving behavior mode 16c has been downloaded. As an alternative to this, it can be provided in a step S13b that already as part of a determination of a route of the motor vehicle 10c , for example from a location outside the geographic area 20c to a target position within the city, that is, within the geographic area 20c , drives, it was checked whether the motor vehicle 10c several predetermined geographic areas when driving along the specific route 20a , 20b , 20c drives through. If several of the specified geographic areas 20a , 20b , 20c are driven through, the driving behavior modes 16 corresponding to the respective given geographic areas 20a , 20b , 20c assigned by the external facility 18th over the communication link 19th in the motor vehicle 10c downloaded. Is the starting position of the motor vehicle 10c for example in the geographical area 20b and the target position in the geographic area 20c , are therefore already in the vehicle system at the start of the journey 12 and / or in one with the vehicle system 12 coupled storage device of the motor vehicle 10c the respective driving behavior modes 16b and 16c deposited.

In a next step S14 if the determined driving behavior mode 16c currently not in the vehicle system 12 is activated, the already activated driving behavior mode 16b deactivated and the determined driving behavior mode 16c activated. This is done in one step S15 the at least partially autonomous driving of the motor vehicle 10c . This takes place taking into account the at least one control parameter 17th the currently activated driving behavior mode 16c . This at least partially autonomous driving of the motor vehicle 10c is here with an arrow in one direction of travel 13 of the motor vehicle 10c in the street 14th outlined. The in Related to 1 described transition route 15th is here only as a transition area between the previous driving behavior mode 16b and the determined driving behavior mode 16c possible if both respective driving behavior modes are already available 16b , 16c in the motor vehicle 10c have been downloaded.

Overall, the examples show a localized driver model and a location-based driver model selection for highly automated driving. In a first part, the invention relates to the knowledge that control, regulation and guidance of an autonomous vehicle of the highest level, that is to say a vehicle designed for fully autonomous driving, should be carried out as a function of a location. It is assumed here that there is an active model in the vehicle that controls the vehicle. This model contains the knowledge about the specific vehicle behavior and the applicable traffic rules. This model is referred to as the driving behavior mode in the context of this patent application 16 designated. The driving behavior mode 16 thus knows what actions have to be taken when the vehicle system 12 receives corresponding sensor signals from sensor units of the vehicle. It is also able to carry out planning, for example for the next several 100 meters in front of the vehicle, that is to say it carries out path planning for a travel route of the vehicle. The route itself from the starting position to the destination position is specified by an external module. This external module is, for example, a control device of a navigation system that sends the corresponding data to the vehicle system 12 transmitted. Since it is expected that fully autonomous vehicles and vehicles will coexist with human drivers for at least a predetermined period of time, it is necessary that the vehicle system 12 the human behavior of the driver and / or other road users is correctly interpreted and predicted. However, every driver who has driven a vehicle in a previously foreign country experiences personally that people behave differently in different parts of the world. For example, China therefore requires a new driving test and does not recognize foreign driving licenses.

If the at least partially autonomous vehicle is to behave like a typical person, it must know, understand and imitate the behavior of a typical person. In this context, behavioral patterns are also relevant that a person learns on site in a driving school, but which cannot be derived from the respective traffic regulations, i.e. from the corresponding legal texts. There is therefore not one driving behavior mode, but several driving behavior modes. These can be implemented classically with the help of a software program or generated by methods of artificial intelligence, i.e. by methods of machine learning.

In a second part, the invention relates to the fact that the individual driving behavior modes 16 depending on the location, i.e. the place where the vehicle is or will be located shortly, in the vehicle. For example, shortly before crossing the border to Portugal, the driving behavior mode is activated 16 for Portugal, i.e. shortly before crossing the border, the typical Portuguese driver is loaded. The same thing happens, for example, when the vehicle enters the urban area of Paris. Then the driving behavior mode 16 activated for the Paris settlement. For example, you can switch between the previous driving behavior mode directly at the border between Spain and Portugal, for example with a corresponding state sign 16 and the driving behavior mode now planned for Portugal 16 respectively. The change can be made soft, for example, so that a continuous transition from the driving behavior mode specified for Spain 16 for the driving behavior mode specified in Portugal 16 over the transition route 15th he follows.

The driving behavior mode 16 or, more precisely, the at least one control parameter 17th that of driving behavior mode 16 includes at least: The applicable traffic rules in the current area, such as left-hand and right-hand traffic, i.e. traffic regulation data 44 ; the not written down but applicable interpretations of this rule, information such as local signs and lane markings are to be interpreted, i.e. the interpretation data 46 ; the typical behavior of other road users at this location, i.e. the road user behavior data 42 , as well as data on the type of vehicle, that is, if necessary, the exact vehicle model, such as the vehicle identification data 48 can be found. A size, i.e. an extent, of the respective locations, i.e. a state, a settlement or a district, as well as the assignment of the individual driving behavior modes 16 is arbitrary and must be used in the context of generating the driving behavior modes 16 , which can also be referred to as learning, can be determined. The driving behavior mode 16 can theoretically already be charged in the vehicle, but it is more likely that the driving behavior mode 16 timely via online connection, i.e. via the communication link 19th , is loaded into the vehicle. It is therefore possible to update it if there are new traffic regulations, for example. They also have the driving behavior modes 16 a not inconsiderable space requirement in a storage device of the Vehicle. It can also be generally assumed that an autonomous vehicle always knows where it is going. The route ahead is therefore known to the vehicle when it starts to drive. The timely download of the appropriate driving behavior mode 16 is therefore easy to plan. This can be done, for example, when planning the route or, for example, only when it is determined that the vehicle is now in a new geographical area 20th is located.

It is now assumed that the driving behavior mode 16 for the vehicle system 12 is generated in the computing device with the help of artificial intelligence, the learning as such would certainly be located in an external computing device, where all relevant vehicle data 30th converge and common learning from all this data takes place. In this context, learning means applying the methods of machine learning to data in order to set the at least one control parameter 17th the specified driving behavior mode 16 for the respective geographic area 20th to determine. At this point it must also be decided which locations, i.e. which geographical areas 20th , its own driving behavior mode 16 get assigned in order to achieve a desired functional quality, i.e. performance, of the method. To do this, the individual driving behavior modes 16 or their respective functional quality for the respective location, that is, for the respective geographical area 20th , be rated. This can be done with a uniform driving behavior mode 16 started, which the known traffic rules are taught, that is, it becomes the ideal behavior model 70 certainly. An evaluation system in the form of the weighting criterion must also be introduced, which makes it possible to test what has been learned for functional quality. This can be a simulation system or a shadow mode functionality in which real vehicles read the previously specified driving behavior mode 16 use. A constant evaluation of the functional quality for different locations will inevitably show that an improvement at a location A is paid for by a deterioration at a location B. This is something that is already being observed today with navigation and assistance systems in vehicles. In this case, the learning system must recognize the conflict of objectives and a location-specific characteristic of the driving behavior mode 16 generate, so that both place A and place B with their own optimal driving behavior mode 16 can be driven on. Because the vehicle is then always with the currently optimal driving behavior mode 16 for the current location, i.e. for the current position 11c , of the vehicle. It is assumed here that the optimal driving behavior mode 16 for Berlin, for example, does not show sufficient functionality in Paris or Shanghai. Therefore, location-specific driving behavior modes are ultimately used 16 for the control of autonomous vehicles that are designed for partially autonomous and / or fully autonomous driving, created and used. Here, local conditions are based on the driving behavior data 40 , the road user behavior data 42 , the interpretation data 46 and the traffic regulation data 44 considered. In addition, a typical behavior is taken into account, which is derived from the driving behavior data 40 and the road user behavior data 42 can be seen, that is, the real behavior model 60 . The evaluation and decision about the localities for the individual driving behavior modes 16 can be created as pertaining to above 2 and 3 has been described. As in 4th described, it can be at a suitable point in time at which the change of location took place, i.e. at the time of the geographical area 20th was changed to an activation of the given, the now entered geographical area 20th assigned driving behavior mode 16 come. In addition, the respective driving behavior modes 16 at exactly the right time, for example via an online service from the external facility 18th loaded into the vehicle, for example before the upcoming change of location, which is known from the vehicle-external navigation systems.

Learning, developing and locating the individual driving behavior modes 16 takes place in the external facility and is made available to the vehicles online. There is also a simulation environment that enables the functional quality to be checked, i.e. within the framework of which the evaluation criterion is determined. Changes to the location and its traffic rules are automatically applied to a new driving behavior mode 16 updated because, for example, new calculations are always made with regard to the specified driving behavior modes 16 can be carried out. Ideally, it is therefore possible that after a first vehicle has been in Portugal, the next vehicle also knows how to typically drive there.

Learning, that is, determining the geographical area 20th and the at least one control parameter 17th the specified driving behavior mode 16 can theoretically also take place in the vehicle itself. In this case, this learning takes place purely locally and without any exchange with other vehicles. Everything related to learning, as it is in the 2 and 3 was described, located in an evaluation device of the vehicle. The evaluation, i.e. the application of the weighting criterion to the real behavior model 60 and the ideal behavior model 70 takes place, for example, in the vehicle and would take into account the current position 11c be generated. The functional quality for a place where the vehicle is not located is again possible by means of simulation, whereby the simulation would also have to take place in the vehicle.

Individual vehicles can also exchange information directly, as can driving behavior modes 16 pass them on to each other without, for example, an external institution 18th to use. In this case there would be a standardization for the driving behavior mode 16 and an associated exchange protocol is required. The standardized driving behavior mode 16 does not take into account the vehicle characteristics, for example 48 . A vehicle coming to Portugal, for example, could then receive information from a local vehicle on how to drive there. This would be done by giving the vehicle newly arriving in Portugal the predetermined driving behavior mode 16 for Portugal is transmitted. In principle, however, the further process steps described remain identical.

Theoretically, it can gradually become a global driving behavior mode 16 which has a complexity that knows every site-specific peculiarity and can therefore actually be used worldwide. It must also be taken into account here that this global driving behavior mode 16 each individual, the geographical areas 20th assigned driving behavior modes 16 includes.

There is also the consideration of offering a fully autonomous vehicle in such a way that it can be controlled remotely and therefore in a situation that requires the driving behavior mode activated in the vehicle 16 cannot cope with a person who works in a call center and has access to the vehicle system 12 of the vehicle is remote-controlled. The corresponding call center employee then has the degree of local knowledge or knows the local traffic rules, as an alternative to an automatically determined and provided driving behavior mode 16 control the vehicle remotely.

Overall, it is a self-learned system that enables fully autonomous driving in a mix with manually driven vehicles and can be used worldwide. The vehicles thus become a typical driver or the driving behavior of a typical driver with the aid of the respective predetermined driving behavior mode 16 made available directly when the vehicle is in the predetermined driving behavior mode 16 assigned geographical area 20th moves.

The method described is particularly suitable for fully autonomous vehicles. However, a driver assistance system designed for partially autonomous driving can also be used as a vehicle system 12 of such localized driving behavior modes 16 benefit. For example, a navigation system, a multimedia device and other preliminary stages for fully autonomous driving, such as a lane departure warning system, a light control, a switching device and so on. It therefore seems likely that the use of several local driving behavior modes 16 is only possible efficiently if methods of artificial intelligence are used, whereby these individual driving behavior modes 16 can be learned. In general, the method described is suitable for all systems that work with models that benefit from local and location-based knowledge. These can't just be vehicles. Currently, mostly market-specific software variants are being created, but especially if the artificial intelligence processes should prevail, a more precise division and optimization depending on the geographical area 20th respectively.

List of reference symbols

10, 10a, 10b, 10c

Motor vehicle

11c

position

12

Vehicle system

13th

Direction of travel

14th

road

15th

Transitional route

16, 16b, 16c

Driving behavior mode

17th

Control parameters

18th

external facility

19th

Communication link

20, 20a, 20b, 20c

geographic area

22nd

State border

24

City limits

30th

Vehicle data

31

Location data

40

Driving behavior data

41

number

42

Road user behavior data

44

Traffic regulation data

46

Interpretation data

48

Vehicle identification data

50

Data pattern

60

Real behavior model

70

Ideal behavior model

S1 to S15

steps

QUOTES INCLUDED IN THE DESCRIPTION

This list of the documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent literature cited

• DE 102012016802 A1 [0004]
• DE 102014217389 A1 [0005]
• DE 102016120508 A1 [0006]

Claims (10)

A method for operating a motor vehicle (10) which comprises at least one vehicle system (12) which is designed to control the motor vehicle (10) at least partially autonomously, with a predefined driving behavior mode (16) being activated in the vehicle system (12), the at least one Control parameters (17) which define driving behavior in a predetermined geographical area (20) and which are taken into account when the motor vehicle (10) is at least partially autonomous, comprising the steps of: - Detecting a current geographical position (11c) of the motor vehicle (10) (S9); - Determining the predetermined geographical area (20) in which the motor vehicle (10) is currently located on the basis of the detected geographical position (11c) (S10); - Determining which predefined driving behavior mode (16) of a plurality of predefined position-dependent driving behavior modes (16) is assigned to the determined geographical area (20) (S11); - Check whether the determined driving behavior mode (16) is already activated in the vehicle system (12) (S12); - If the determined driving behavior mode (16) is currently not activated in the vehicle system (12), deactivating the already activated driving behavior mode (16) and activating the determined driving behavior mode (16) (S14); and - At least partially autonomous control of the motor vehicle (10) taking into account the at least one control parameter (17) of the currently activated driving behavior mode (16) (S15). Method according to the preceding claim, wherein the at least one control parameter (17) is determined from data of at least one of the following types of data: - Traffic rule data (44) which characterize at least one traffic rule currently applicable in the geographic area (20); - Driving behavior data (40) which characterize the driving behavior typical of a driver of a vehicle in the geographic area (20); - Road user behavior data (42) which characterize a behavior that is typical for at least one road user in the geographic area (20); - interpretation data (46) which characterize an interpretation rule for at least one traffic symbol in the geographic area (20); - Vehicle identification data (48) which characterize the motor vehicle (10). Method according to the preceding claim, wherein the geographical area (20) for the respective driving behavior mode (16) is determined by the following steps: - Provision of vehicle data (30) from at least one vehicle which is operated by the respective driver and which records the vehicle data (30) with at least one detection device of the vehicle, the respective vehicle data (30) describing at least one driving maneuver of the at least one vehicle and additionally location data (31) characterizing a detection location of the vehicle data (30) comprise (S1); - Determining the driving behavior data (40) and / or the road user behavior data (42) by applying a method of machine learning to the vehicle data (30) (S2) provided; - Recognition of at least one data pattern (50) in the determined driving behavior data (40) and / or the determined road user behavior data (42) by applying the machine learning method to the determined driving behavior data (40) and / or the determined road user behavior data (42) (S3 ); - Establishing an extent of the respective geographical area (20) taking into account the at least one recognized data pattern (50) and the location data (31) (S4) comprised by the vehicle data (30); Procedure according to the Claims 2 and 3 , wherein a definition of the at least one control parameter (17) of the predetermined driving behavior mode (16) for the respective geographical area (20) comprises the following steps: - Defining a real behavior model (60) for the specific respective geographical area (20) taking into account the determined driving behavior data (40) and / or road user behavior data (42) which are assigned to the respective defined geographical area (20) (S5); - Provision of the traffic regulation data (44), interpretation data (46) and / or vehicle identification data (48) which are assigned to the defined geographical area (20) (S6); - Establishing an ideal behavior model (70) for the at least one motor vehicle (10) in the specified respective geographical area (20) taking into account the provided traffic control data (44), interpretation data (46) and / or vehicle identification data (48) that correspond to the respective specified geographical area Area (20) are assigned (S7); - Determination of the at least one control parameter (17) for the respective geographical area (20) from the real behavior model (60) and ideal behavior model (70) defined for the respective defined geographical area (20) by applying a predetermined weighting criterion (S8). Method according to the preceding claim, wherein the weighting criterion by a Simulation and / or a shadow mode function is determined. Method according to one of the preceding claims, wherein, as part of a determination of a travel route from the current geographical position (11b) to a predetermined target position of the motor vehicle (10), a check is made as to whether the motor vehicle (10) drives along the determined route several predetermined geographic Areas (20) and, if several predetermined geographic areas (20) are traversed, the driving behavior modes (16) assigned to the respective predetermined geographic areas (20) from an external device (18) via a communication link (19) can be downloaded into the motor vehicle (10). Method according to the preceding claim, wherein a transition from the previous driving behavior mode (16) to the determined driving behavior mode (16) takes place while the motor vehicle (10) is driving over a predetermined transition route (15). Method according to one of the Claims 1 to 5 wherein the determined driving behavior mode (16) is downloaded from an external device (18) via a communication connection (19) into the motor vehicle (10) as soon as it is determined that the motor vehicle (10) is assigned to the determined driving behavior mode (16) geographical area (20) is located. Method according to one of the preceding claims, wherein the geographic area (20) comprises at least one of the following areas: - a state; - a member state; - a district; - a settlement. Motor vehicle (10) with a vehicle system (12) which is designed to control the motor vehicle (10) at least partially autonomously, the vehicle system (12) or a control device coupled to the vehicle system (12) being designed to implement a method according to one of the perform preceding claims.

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