DE102023118703A1 - Procedure for using at least two different digital maps - Google Patents

Abstract

Method for using at least two different digital maps (1, 2) in a vehicle (3), comprising the following steps: a) reading in a first digital map (1); b) carrying out a first localization (4) of the vehicle (3) in the first digital map (1) using a localization method (6); c) reading in a second digital map (2); d) reading in transformation data (7) which enable the first localization (4) to be converted into a second localization (5) of the vehicle (3) in the second digital map (2); and e) carrying out a second localization (5) of the vehicle (3) in the second digital map (2), the first localization (4) and a transformation (8) being used to carry out the second localization (5), the transformation (8) being carried out using the transformation data (7) read in in step d).

Description

State of the art

This describes a method for using at least two different digital maps in a vehicle.

Digital maps play a crucial role in the implementation of assisted and automated driving functions in vehicles. Digital maps are data sets that describe the situation along a vehicle's route and, as a supplement to environmental data obtained in real time, enable a significantly improved implementation of assisted and automated driving functions in the vehicle than would be possible based solely on environmental data obtained in real time.

Such digital maps contain (in contrast to simpler digital navigation maps used by road navigation systems for road navigation) data relating to the situation in the immediate vicinity of the route, which to a certain extent enables the assisted and automated driving functions to look beyond the visibility horizon of the environmental sensors in the vehicle and to be able to better assess and evaluate the environmental data obtained with the environmental sensors in the vehicle. If, for example, a certain traffic situation in the immediate vicinity of the vehicle has been detected with the environmental sensors, then this situation can be better assessed with the help of data from a digital map that describes the further course of the road. For example, conclusions can be drawn as to how the traffic situation is likely to develop as the journey continues along the road.

Such digital maps regularly contain a significant amount of data describing the route and its immediate surroundings. Such as route history data and road markings, data on objects in the vicinity of the route, road topology and geometry information at lane level, and information on semantic context, such as traffic signs and traffic light positions. The accuracy and wealth of information in such maps regularly exceeds the amount of information that can be acquired using sensors alone. Extracting comprehensive semantic information directly from raw sensor data in real time is a major challenge, particularly in complex traffic scenarios. By using digital maps that contain the data mentioned as examples, the environmental data obtained with environmental sensors can be enriched and evaluated more efficiently. In particular, by using digital maps, in many situations or for many pieces of information it is only necessary to correctly evaluate the information contained in digital maps using the sensor data.

Such digital maps (unlike simple digital navigation maps used by road navigation maps for road navigation) are usually not available in a uniform format for the entire road network. Such digital maps are provided by a wide variety of service providers. Unlike simple digital navigation maps, such digital maps are often not available in a fixed, uniform global coordinate system. The data underlying such digital maps is often acquired by test drives of vehicles along routes/roads. Very individual coordinate systems are often used to describe the position of the data in the map, which relate, for example, to the position and, if applicable, the orientation of the vehicle that carried out the test drive. In this case, there is a coordinate system that relates to the route. It is often not possible to convert the digital maps or the position information for individual objects contained in the digital maps exactly into a uniform global coordinate system. This is often because during the acquisition of the data on which the digital map is based, no exact positions are available that would be necessary for converting position information into a global coordinate system, for example because no GNSS signal is available or the quality of the GNSS during acquisition is insufficient to determine an exact positioning. A very typical example would be a digital map that describes the surroundings of a motor vehicle in a tunnel. Here, no GNSS is available at all. Nevertheless, digital maps are needed for assisted and automated driving functions in such a situation.

In many situations, there are also different digital maps available, which come from different data service providers, for example, and which sometimes contain complementary information. For a particularly advantageous execution of assisted and highly automated driving functions, it is desirable to be able to access the information from different digital maps in combination, if different digital maps are available.

In order for a digital map in a vehicle to be used for assisted and highly automated driving functions In order for the system to be used sensibly, the vehicle must be localized on the digital map. The term "localization" here means determining the exact position of the vehicle on the digital map.

However, the computing power required to process such digital maps and in particular the localization in the digital map required to process the map for assisted and highly automated driving functions is considerable.

The object of the present invention is to at least partially solve the problems described with reference to the prior art. This object is achieved with the method described below for using at least two different digital maps in a vehicle.

This object is achieved with the invention according to the features of the independent patent claims. Further advantageous embodiments are specified in the dependently formulated patent claims as well as in the description and in particular in the description of the figures. It should be noted that the person skilled in the art can combine the individual features in a technologically meaningful way and thus arrive at further embodiments of the invention.

1. a) Einlesen einer ersten digitale Karte;
2. b) Durchführen einer ersten Lokalisierung des Fahrzeugs in der ersten digitalen Karte mit Hilfe eines Lokalisierungsverfahrens;
3. c) Einlesen einer zweiten digitalen Karte;
4. d) Einlesen von Transformationsdaten, die eine Umrechnung der ersten Lokalisierung in eine zweite Lokalisierung des Fahrzeugs in der zweiten digitalen Karte ermöglichen; und
5. e) Durchführen einer zweiten Lokalisierung des Fahrzeugs in der zweiten digitalen Karte, wobei für die Durchführung der zweiten Lokalisierung die erste Lokalisierung und eine Transformation verwendet wird, wobei die Transformation mit Hilfe der in Schritt d) eingelesenen Transformationsdaten erfolgt.

This describes a method for using at least two different digital maps in a vehicle, comprising the following steps:

1. a) Reading a first digital card;
2. b) performing a first localisation of the vehicle in the first digital map using a localisation method;
3. c) reading a second digital card;
4. d) reading in transformation data that enable the first location to be converted into a second location of the vehicle in the second digital map; and
5. e) carrying out a second localization of the vehicle in the second digital map, wherein the first localization and a transformation are used to carry out the second localization, wherein the transformation is carried out with the aid of the transformation data read in in step d).

Reading the digital cards in steps a) and c) includes in particular the extraction of data from the cards from a data storage device, whereby data is extracted which is required to carry out the further steps of the method.

Carrying out the localization in step b) here means the step of using additional data that is suitable for establishing a spatial relationship between the vehicle and the digital map to determine where the vehicle is located within the representation of the environment in the digital map. The term "pose" is also used for localization. The pose usually includes a position specification that consists, for example, of two coordinates on a surface or three coordinates in a space, as well as information on an orientation. The orientation can, for example, be specified in the form of angle values that describe the orientation of the vehicle relative to axes. If the localization includes a position on a surface, for example, an angle value is specified relative to an axis that is orthogonal to the surface. If the localization includes a position in space, for example, three angle values are specified relative to three spatial axes that are orthogonal to one another. In principle, localizations or poses always include position information and angle values. Depending on the design of the coordinate system of the respective digital map, however, these values can be defined very differently. For example, it is conceivable that a digital is described in a coordinate system that describes the arrangement of objects along a central line of a road. Such a coordinate system is also called a "distance coordinate system" here. Position information can then be, for example, distance information along this central line as well as distance information, where the distance information defines, for example, a distance of the position from this central line. An orientation can then also be defined relative to a direction of this central line. For example, an orientation in angular values can indicate how much the orientation of the respective vehicle in space deviates from the direction of the central line at the respective position.

Digital maps have already been explained above. Digital maps are particularly high-resolution (HD) planning and localization maps, which are regularly essential for highly automated driving from level 2+. Such digital maps form a knowledge base for highly automated driving functions. The current location and position of the automated vehicle can be determined by comparing certain features recorded by on-board environmental sensors with the map. In highly automated driving functions, planning is often carried out following localization, which makes certain behavior and movement decisions for the vehicle on the basis of a planning map. In contrast to the localization map, the planning map contains map also regularly includes road topology and geometry information at lane level as well as semantic context, such as traffic signs and traffic light positions. The accuracy and abundance of information in these two types of maps regularly exceeds the sensor performance of environmental sensors.

Localization procedures carried out in step b) are used to find the position or orientation. Various localization procedures will be explained in more detail later.

Transformation data read in step d) and processed in step e) are data that enable the conversion of a first localization in a first digital map into a second localization in a second digital map without further access to external data (e.g. sensor data, etc.) being required. If a first digital map is, for example, a localization map that describes important landmarks along a route in absolute global coordinates (e.g. GNSS coordinates), then a localization process for determining a first localization can be carried out, for example, with the help of environmental data, so that an exact GNSS coordinate of the vehicle is determined as the first localization based on the important landmarks. If a second digital map is a planning map that describes the information required for planning driving maneuvers in a route coordinate system, then the transformation data can, for example, be data that enable the conversion of an exact GNSS coordinate into a position in the route coordinate system of the second map.

The advantage of the described method is that several localizations are determined in different digital maps, whereby a localization process only needs to be carried out once due to the transformation data. The calculations for calculating/carrying out the second localization using the transformation data are usually much simpler than directly carrying out a localization process corresponding to step b) in the second digital map.

It is particularly advantageous if the localization method carried out in step b) carries out the first localization at least partially with the aid of environmental data, wherein the environmental data were generated by at least one environmental sensor of the vehicle and wherein in the localization method markers in the first digital map are recognized on the basis of the environmental data and the first localization takes place relative to the markers.

The basic difficulty in implementing localization procedures is that the vehicle itself does not have the technology to carry out highly accurate localization using GNSS. For this reason, the localization described here using environmental data is often preferred, as it allows much more precise position information to be determined (with sufficiently precise localization maps).

Environmental data is preferably data that was obtained using environmental sensors in the vehicle. Environmental sensors are, for example, cameras, lidar sensors and/or radar sensors. Other types of sensors are also conceivable. Environmental data preferably contains information that describes the appearance of objects in the vehicle's environment from the perspective of the respective sensor. For example, an image from a camera can show a lane marking strip on a road that is in the immediate vicinity of the vehicle. This lane marking strip is preferably also stored in the digital map. An exact position for the lane marking strip is preferably also stored in the digital map - for example in the form of GNSS coordinates.

The localization process can now be used to compare the lane marking strip from the camera image with the lane marking strip stored on the map. By knowing the exact position and orientation of the camera that took the image on the vehicle, the relative distance and orientation of the vehicle to the lane marking strip can now be determined. The location of the vehicle can now also be determined from the position of the lane marking strip on the digital map.

It is also advantageous if the first localization is carried out at least partially with the help of GNSS data, which are used to localize the vehicle in coordinates of the first digital map.

As we have already described, there is often the problem that vehicles often do not have the technology to carry out highly accurate GNSS localization. The GNSS systems available in vehicles can often only achieve accuracies in the range of a few meters, whereas regularities and localization accuracies in the centimeter range are required for the operation of highly automated driving functions. Nevertheless, localization using GNSS data is basically possible, especially if highly accurate GNSS systems are available in vehicles.

However, it is often particularly advantageous when both localization approaches (GNSS localization and localization using environmental data) are used together. A vehicle's position can often be roughly determined using GNSS, and then a more precise classification of the localization can be made using environmental data.

With the help of the rough predetermination, it is possible, for example, to select landmarks and markers in the vicinity of the vehicle that may be helpful for precise localization with the help of environmental data, in order to then carry out the precise localization with the help of the environmental data and the landmarks or markers.

• - In den jeweiligen digitalen Karten müssen für die Durchführung des Lokalisierungsverfahrens geeignete Marker bzw. Landmarken überhaupt vorhanden sein. Insbesondere müssen ggf. sogar spezielle Karten verwendet werden, die extra solche Landmarken bereitstellen. Solche Karten werden regelmäßig auch als Landmarken-Karten bezeichnet.
• - Landmarken bzw. Landmarkenkarten beinhaltend die Landmarken haben regelmäßig sehr viele Daten. Die Übertagung der Landmarkenkarte ins Fahrzeug und Speicherung ist teuer.
• - Die Signalverarbeitung zur Auswertung von Umfelddaten mit dem Ziel einer Lokalisierung gemäß den weiter vorne beschriebenen Ansätzen ist kompliziert, rechenaufwendig, und eine aufwändige Signal-Verarbeitung.

In most cases, the following disadvantages can be assumed for localization using environmental data:

• - The respective digital maps must contain suitable markers or landmarks for the localization process to be carried out. In particular, special maps may have to be used that provide such landmarks. Such maps are also regularly referred to as landmark maps.
• - Landmarks or landmark maps containing the landmarks usually contain a lot of data. Transferring the landmark map to the vehicle and storing it is expensive.
• - The signal processing for evaluating environmental data with the aim of localization according to the approaches described above is complicated, computationally intensive, and requires complex signal processing.

• - erfordert spezielle HW z.B. Antennen, Receiver (teuer!)
• - Kosten für Korrektursignale, bei der Verwendung von Differential-GPS
• - Aufwändige Signal-Verarbeitung

For localization using GNSS positions, the following disadvantages can be assumed for the vast majority of cases:

• - requires special HW e.g. antennas, receivers (expensive!)
• - Costs for correction signals when using differential GPS
• - Complex signal processing

The disadvantages of the two approaches can be well compensated by combined approaches in which a rough localization is first carried out using GNSS and then a more precise localization is carried out using environmental data.

It is advantageous if the first digital map and/or the second digital map were generated based on data acquired using a crowd-based process during journeys of vehicles in regular operation.

It is also advantageous if the first digital map and/or the second digital map were generated based on data acquired from special test vehicles during test drives. Such approaches to obtaining data for a digital map are also referred to as "survey" approaches.

In survey approaches, data for creating the map is acquired using special survey vehicles. Such vehicles can be equipped with complex reference measurement technology specifically for this purpose, which enables high-quality data collection.

Routes that are driven to collect data can be specified and planned. Due to the effort involved, not all roads can normally be recorded in this way. Typically, with "survey approaches" each road is only driven once or only at a clearly limited repetition rate in order to collect data. Basically, with "survey approaches" raw data is collected, which is then processed to generate the actual map. It is not necessary to collect and transmit the data live. Instead, it is possible to first store the data in the vehicle and then process it, also saving the raw data. Due to the large amount of data, this is not possible in the crowd approaches described in more detail below. The data processing to generate the map preferably includes a combination of manual and automatic processing.

The advantages of maps obtained using "survey approaches" are, first of all, the very high level of accuracy that can be achieved by using particularly complex GNSS measurement technology in the vehicles. The ability to use particularly good measurement technology also makes it possible to realize rare map attributes that would not be possible to determine using crowd approaches. The availability of the raw data means that all processing steps of the raw data to generate the map can be repeated and corrected. The possibilities for quality assurance and quality assessment of the quality of the map are significantly increased.

Basically, the disadvantage of “survey approaches” is that the number of survey vehicles available is limited. The effort and costs of collecting data with survey vehicles are high. Due to the fact that the effort has to drive all the roads is often too high, maps obtained using "survey approaches" can usually only provide partial coverage of the existing road network. The number of updates and the timeliness are also limited by the effort involved.

There are various providers on the market that provide maps obtained using “survey approaches”.

A distinction must be made between so-called “survey approaches” and “crowd approaches”, in which the data for creating the map comes from end-user vehicles, which regularly and permanently collect data using the sensors in the vehicle and make it available for the creation of maps. From the perspective of map creation, the routes and streets traveled are chosen rather randomly because they are determined by the driving behavior of the end users. The sensors used here to collect the data are usually of lower quality than those used in “survey approaches” and were also originally/mainly intended for other purposes and operating methods. In principle, however, crowd approaches can enable a large number of vehicles to contribute to obtaining data for maps. Data is regularly obtained on almost all roads. Regularly and permanently updated data is provided.

Raw data from the sensors is normally processed in the vehicle itself, and this creates aggregated or compressed data that is then transmitted by the respective vehicle. The first step in data processing (in the vehicle) is therefore always automatic. If necessary, automatic processing steps can be supplemented by manual measures in the further processing of the data.

The biggest advantage of crowd approaches is that there are a large number of vehicles available for data collection and therefore a large amount of data. Overall, the effort required to collect data is very low compared to survey approaches. The good coverage and high level of timeliness are also clear advantages of crowd approaches.

The disadvantage of such crowd approaches is the fundamentally lower accuracy because only the sensors already present in the vehicle are available. This applies in particular to the GNSS used, which can often be considerably more accurate in survey vehicles. The map attributes that can be obtained are also limited in crowd approaches compared to survey approaches because no special sensors can be used. Raw data for testing and quality control are often not available (as described above). Various providers are also available for maps generated using crowd approaches.

Basically, it is clear that both approaches have different properties. In today's assistance systems and in automated driving, both survey and crowd maps are therefore often used simultaneously. It is also conceivable to use several crowd or survey maps in parallel.

Furthermore, it is advantageous if the transformation data read in step d) were determined by performing a first localization in the first digital map using a localization method and a second localization in the second digital map using a localization method, wherein transformation data were calculated from the first localization and the second localization.

Preferably, transformation data are determined in the localization process carried out with both digital maps for a specific position of the vehicle in parallel. Matching first localizations in the first digital map and a second localization in the second digital map are then available independently of the transformation data. Transformation data can then be calculated backwards from the second localization and the first localization.

It is also advantageous if the first localization and the second localization for calculating the transformation data were acquired using a crowd-based method during journeys of vehicles in regular operation.

During journeys in regular operation, there are situations in which the hardware for carrying out calculations (which are required for localization, for example) is less loaded - for example because a driver of the vehicle is currently driving himself, highly automated driving functions are deactivated, etc. Such situations can be used to carry out additional localization independently of transformation data in another map. According to the example given above, a first localization and a second localization can each be carried out using traffic signs and/or road markings. Both localizations can then be used to determine transformation data that enable a conversion from the first localization in the first digital map to the second localization in the second digital map. Such transformation data can be used if necessary. are calculated directly in the vehicle and then made available to data service providers via an interface. It is also possible that such transformation data is not determined in the vehicle itself, but the (related) localizations are transmitted as a data packet via an interface and suitable transformation data is then created externally (by a data processor) in order to then be able to make them available to other road users.

Furthermore, it is advantageous if the first localization and the second localization for calculating the transformation data were acquired from special test vehicles on test drives.

In particular, test drives to determine transformation data make it possible to use highly accurate GNSS data directly to determine the transformation data, because hardware to determine such highly accurate GNSS data is usually only available in test vehicles.

It is particularly preferred if a combination of environmental sensors or environmental data and GNSS is used to determine transformation data. For example, markers or landmarks in various digital maps can be recognized with the help of environmental sensors and environmental data and highly accurate GNSS data can be used to link these markers or landmarks from the various maps with one another or to assign positions of the markers or landmarks to one another.

It is also advantageous if at least the first digital map, the second digital map and/or the transformation data are provided by a data service provider and read into the vehicle via an online function.

The online function of a vehicle is preferably a data interface, which is implemented, for example, using common mobile transmission technologies (4G, 5G, etc.). The online function can be used to read data from various service providers into the vehicle while the vehicle is in operation. If necessary, the online function can be used to establish a direct connection to a service provider for the operation of the respective vehicle. This can be, for example, the vehicle manufacturer itself and/or a partner of the vehicle manufacturer. This service provider then makes all data useful for the operation of the vehicle available via the online function. In design variants, the online function can also enable direct access to various service providers, for example by providing free access to the Internet.

Service providers of digital maps are, for example, places that centrally offer prepared digital maps (as described above). Preferably, the maps offered by such service providers are always very up-to-date. The total amount of data in a digital map such as the localization maps and planning maps described above for a specific region, for example for Germany, is very large. For this reason and for reasons of timeliness, such digital maps are preferably only accessed in sections and only for certain parts of regions or, particularly preferably, only for certain roads on which the vehicle is driven.

Furthermore, it is advantageous if at least one of the method steps a), c) and d) are carried out in advance for a planned route of the vehicle, wherein the method steps b) and e) are regularly repeated during the journey on the route in order to regularly update the first localization and the second localization.

Such an approach achieves a compromise between the amount of data retrieved from the digital maps and the availability of the data in the vehicle. The availability of the digital maps is often very important for the safe operation of highly automated driving functions. For this reason, it is advantageous to retrieve the data from the digital maps and preferably also the transformation data in advance and store them in the vehicle. A temporary failure of an online function then does not affect the availability of the data mentioned in the vehicle.

Particularly preferably, a route plan that has been carried out in a navigation system by the driver/operator of the vehicle is used as a basis for selecting partial data from the digital map data and transformation data available from the service provider and reading them into the vehicle.

It is advantageous if the transformation data is provided as an additional data layer with the first digital map and/or the second digital map.

Particularly preferably, transformation data are provided by the same service providers as the respective digital map in which localizations are determined using the transformation data. Transformation data can preferably be provided as part of the respective digital map.

In embodiments of the method, it is possible that the transformation data for carrying out step e) are interpolated from several transformation data sets based on a first localization.

Deviations between the coordinate systems of different digital maps often do not change abruptly, but rather they often increase and decrease gently or softly in transition areas. For example, if a GNSS position determination was incorrect or had a relatively large error during a test drive to determine data from a digital map, then a coordinate system of this digital map may be shifted over a wide range. For this reason, it is not usually necessary for transformation data to have a very high data density. Rather, interpolation of transformation data sets can often be used effectively to increase the data density.

The method is particularly advantageous if steps c), d) and e) are repeated for a further digital map with further transformation data in order to obtain a further localization of the vehicle in the further digital map.

In principle, the method is suitable for the parallel use of any number of digital maps, provided that transformation data is available for each of these digital maps.

Also to be described here is a device for data processing, comprising at least one processor which is configured to carry out the described method.

Such a device preferably also has at least one digital memory for storing program components that are required to carry out the described method, as well as for storing data that is processed using the described method. Such a device preferably also has interfaces via which the data required to carry out the described method can enter the device - for example the digital maps, transformation data and/or environmental data.

Also to be described here is a computer program product comprising instructions which, when the computer program product is executed by a computer, cause the computer to carry out the described method.

Such a computer program product can, for example, be stored in the digital memory of a described device.

In addition, a computer-readable storage medium is to be described, comprising instructions which, when executed by a computer, cause the computer to carry out the described method.

Furthermore, a vehicle comprising a described device for data processing is to be described.

The device (and the vehicle) are preferably configured to carry out the described method. The described method is preferably used in the vehicle to obtain (highly accurate) localizations of the vehicle in various available digital maps.

• 1: ein Beispiel für eine Situation eines Fahrzeugs in einer Umgebung, die durch digitale Karten beschrieben ist;
• 2: ein Ablaufdiagramm des beschriebenen Verfahrens; und
• 3: ein Kraftfahrzeug mit einer Vorrichtung zur Durchführung des beschriebenen Verfahrens.

The invention and the technical environment of the invention are explained in more detail below with reference to the figures. The figures show preferred embodiments to which the invention is not limited. It should be noted in particular that the figures and in particular the proportions shown in the figures are only schematic. They show:

• 1 : an example of a situation of a vehicle in an environment described by digital maps;
• 2 : a flow chart of the described procedure; and
• 3 : a motor vehicle with a device for carrying out the described method.

1 shows a vehicle 3 which is located in an environment. The real situation 11 here refers to the environment of the motor vehicle which can be detected by the environment sensors 12 of the vehicle 3. The real situation 11 here comprises a roadway marked with solid lines, on the edge of which a total of three real objects 14 are shown. The real objects 14 can be, for example, street signs, houses, road markings or similar objects. Indicated here by dashed lines for these real objects 14 are environmental data 9 which can be determined by the environment sensors 12 of the vehicle 3 and with which a real localization 10 of the vehicle 3 is possible. In other words, the term “real localization” means that it is determined on the basis of the environmental data 9 where which position the vehicle 3 is located relative to the real objects 14.

In 1 A first digital map 1 (with even lines) and a second digital map 2 (with a dash-dot line) are indicated by dashed lines. The first digital map 1 and the second digital map 2 each show the roadway and markers 13, 13' in the first digital map 1 and in the second digital map 2. The markers 13, 13' each correspond to the real objects 14 in the real situation 11.

The first digital map 1 and the second digital map 2 describe the real situation 11 quite precisely. However, in a global coordinate system (e.g. in GNSS coordinates), the first digital map 1 and the second digital map 2 do not coincide with the real situation 11. This is in the 1 represented by the fact that both the first digital map 1 and the second digital map 2 are each shown offset from the real situation 11. This can be due, for example, to the fact that when the first digital map 1 and/or the second digital map 2 were created, no exact position data in global coordinates were available, for example because a situation is described in which an exact determination of position data was not possible at all (e.g. in a tunnel) or because the data for the respective digital map was determined in a crowd-based process with a vehicle that does not have the appropriate sensors to obtain exact position data in global coordinates.

Basically, there can be various reasons why deviations exist between the coordinate systems of different digital maps.

Due to the deviations between the coordinate systems of different digital maps, localizations of the vehicle 3 must be carried out in order to be able to use digital maps 1, 2 for highly automated driving functions of the vehicle 3. For each digital map 1, 2 used, it must be known where the vehicle 3 actually is within this digital map 1, 2. A first localization 4 in the first digital map 1 and a second localization 5 in the second digital map 2 are indicated schematically here.

The first localization 4 is carried out here using a localization method 6. The localization method 6 is indicated schematically here by arrows that point to the markers 13 in the first digital map 1. These arrows correspond to the environmental data 9 indicated by dashed lines, which correspond to the relative position of the vehicle 3 to the real objects 14. Since the markers 13 correspond to the real objects 14, the first localization 4 can be carried out using the localization method 6.

As we have already indicated above, such a localization method 6 for creating a first localization 4 in a first digital map 1 is complex. For example, the real objects 14 must be recognized from the environmental data 9 and an assignment to the markers 13 must be carried out. These steps are usually very computationally intensive, as already explained above.

For this reason, it is proposed here not to obtain the second localization 5 in the second digital map 2 with a localization method 6, but with the help of a transformation 8, which is carried out with the help of transformation data 7. Transformation data 7 contain information on how first localizations 4 in the first digital map 1 can be converted into second localizations 5 in the second digital map 2. Transformation data 7 for carrying out the transformation 8 are made available for the described method and are determined in advance for the digital maps 1, 2, for example.

In 1 Markers 13' can also be seen in the second digital map 2, which correspond to the real objects 14 and the markers 13 in the first digital map 1. The transformation 8 means that the relative position of the vehicle 3 to the markers 13' in the second digital map 2 does not have to be determined. The transformation 8 can be used to access the first localization 4 in the first digital map 1 determined using the localization method 6.

The 2 shows a flow chart of the described method with the method steps a), b), c), d) and e), which are carried out one after the other, wherein the method steps a), c) and d) are preferably carried out in advance so that the digital maps 1, 2 and transformation data 7 are stored in the vehicle 3 for a planned upcoming journey. The method steps b) and e) are then carried out permanently while the vehicle 3 is travelling in order to localise the vehicle 3 within the maps 1, 2.

The 3 shows a vehicle 3 with a device 15 for carrying out the described method with a processor 16 which is configured to carry out the steps of the described method and possibly further components (not shown separately here) for carrying out the described method.

list of reference symbols

1

first digital map

2

second digital map

3

vehicle

4

first localization

5

second localization

6

localization process

7

transformation data

8

transformation

9

environmental data

10

real localization

11

real situation

12

environment sensor

13

marker

14

real object

15

device

16

processor

Claims (17)

Method for using at least two different digital maps (1, 2) in a vehicle (3), comprising the following steps: a) reading in a first digital map (1); b) carrying out a first localization (4) of the vehicle (3) in the first digital map (1) using a localization method (6); c) reading in a second digital map (2); d) reading in transformation data (7) which enable the first localization (4) to be converted into a second localization (5) of the vehicle (3) in the second digital map (2); and e) carrying out a second localization (5) of the vehicle (3) in the second digital map (2), the first localization (4) and a transformation (8) being used to carry out the second localization (5), the transformation (8) being carried out using the transformation data (7) read in in step d). procedure according to claim 1 , wherein the localization method (6) carried out in step b) carries out the first localization (4) at least partially with the aid of environmental data (9), wherein the environmental data (9) were generated by at least one environmental sensor (12) of the vehicle (3) and wherein in the localization method (6) markers (13) in the first digital map (1) are recognized on the basis of the environmental data (9) and the first localization (4) takes place relative to the markers (13). procedure according to claim 1 or 2 , wherein the first localization (4) is carried out at least partially with the aid of GNSS data, with which a localization of the vehicle (3) is carried out in coordinates of the first digital map (1). Method according to one of the preceding claims, wherein the first digital map (1) and/or the second digital map (2) were generated based on data acquired by means of a crowd-based method during journeys of vehicles (3) in regular operation. Method according to one of the preceding claims, wherein the first digital map (1) and/or the second digital map (2) were generated based on data acquired from specific test vehicles during test drives. Method according to one of the preceding claims, wherein the transformation data (7) read in in step d) were determined by a first localization (4) in the first digital map (1) using a localization method (6) and a second localization in the second digital map (1) using a localization method (6), wherein transformation data (7) were calculated from the first localization (4) and the second localization (5). procedure according to claim 6 , wherein the first localization (4) and the second localization (5) for calculating the transformation data were acquired using a crowd-based method during journeys of vehicles (3) in regular operation. procedure according to claim 6 , whereby the first localization (4) and the second localization (5) for calculating the transformation data (7) were acquired from special test vehicles on test drives. Method according to one of the preceding claims, wherein at least the first digital map (1), the second digital map (2) and/or the transformation data (7) are provided by a data service provider and are read into the vehicle (3) via an online function. Method according to one of the preceding claims, wherein at least one of the method steps a), c) and d) are carried out in advance for a planned route of the vehicle (3), wherein the method steps b) and e) are regularly repeated during the journey on the route in order to regularly update the first localization (4) and the second localization (5). Method according to one of the preceding claims, wherein the transformation data (7) are provided as an additional data layer with the first digital map (1) and/or the second digital map (2). Method according to one of the preceding claims, wherein the transformation data (7) for carrying out step e) are interpolated from a plurality of transformation data sets based on a first localization (4). Method according to one of the preceding claims, wherein steps c), d) and e) are repeated for a further digital map with further transformation data in order to obtain a further localization of the vehicle (3) in the further digital map. Device (15) for data processing, comprising at least one processor (16) configured to carry out the method according to one of the Claims 1 until 13 executes. Computer program product comprising instructions which, when the computer program product is executed by a computer, cause the computer to carry out the method according to one of the Claims 1 until 13 to execute. Computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the method according to one of the Claims 1 until 13 to execute. Vehicle (3) comprising a device (15) for data processing according to claim 14 .

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