Method and system for determining the own position of a motor vehicle and motor vehicle equipped with such a system

Abstract

The invention relates to a method and a system (1) for determining a self-position (2) of a motor vehicle (3), wherein a map data set (10) of the surroundings (11) of the motor vehicle (3) is provided to a data processing unit (4), and at least one surroundings element (12) in the surroundings (11) is detected at least partially by means of a sensor device (5) of the motor vehicle (3), and a sensor device data set (13) at least partially characterizing the surroundings (11) is provided to the data processing unit (4), so that the data processing unit (4) generates and provides a self-position data set (14) from the sensor device data set (13) and the map data set (10). According to the invention, a context data set (15) is provided to the data processing unit (4), by means of which the data processing unit (4) subjects the self-position data set (14) to a plausibility check. The invention also relates to a motor vehicle (3) equipped with such a system (1).

Description

TECHNICAL FIELD

[0001] The invention relates to a method for determining a self-position of a motor vehicle, wherein a map data set of a surrounding of the motor vehicle is provided to a data processing unit and at least one surrounding element in the surrounding is detected at least partially by means of a sensor device of the motor vehicle and a sensor device data set at least partially characterizing the surrounding is provided to the data processing unit, so that the data processing unit generates and provides a self-position data set from the sensor device data set and the map data set. The invention furthermore relates to a system for determining a self-position of a motor vehicle, which is configured to carry out such a method. The invention furthermore relates to a motor vehicle, which is equipped with such a system. BACKGROUND

[0002] Today, there are far-reaching efforts in the field of motor vehicle technology to automate motor vehicles, in particular cars, i.e. passenger cars and / or lorries, to an ever greater extent in order to relieve the driver of a motor vehicle or passenger car or lorry to an ever greater extent from the driving task. Here, according to SAE J3016, the respective degree of automation or autonomy is divided into five levels. In particular in connection with levels 3, 4 and 5, in which the driver of a motor vehicle can at least temporarily avoid the current driving task and / or the current traffic situation, there is of course the need to configure motor vehicles that are automated according to level 3, 4 and / or 5 in particular safely and reliably.

[0003] In the case of a highly automated motor vehicle (level 3), the driver is allowed to at least temporarily and / or in certain pre-set or specific application situations of the motor vehicle to avoid the driving task. This means that the motor vehicle then drives independently or carries out the driving task itself. For example, a motor vehicle that is highly automated according to level 3 is configured to control the steering and drive units of the motor vehicle independently or automatically, i.e. without the help of the driver, in order to complete the driving task.

[0004] In a fully automated motor vehicle (level 4), the driver is allowed to at least temporarily, for example for the entire duration of a specific application situation, to completely entrust the completion of the driving task to the motor vehicle - the driver is then only a passenger in the motor vehicle. In the case of an autonomous motor vehicle (level 5), a human driver is not provided at all. This means that with an autonomous motor vehicle according to level 5, only passengers are transported, wherein these passengers do not control or supervise / monitor the driving task of the motor vehicle at any time.

[0005] In order to make motor vehicles which can be operated or driven autonomously from level 3 particularly safe and reliable, there is a need for particularly safe or autonomous navigation of the motor vehicle. For this, it is necessary to determine or ascertain the current position of the respective motor vehicle particularly reliably. The prior art provides systems for this, for example based on a comparison of image data detected by means of vehicle cameras with high-precision digital maps on the basis of landmarks, for example ground markings, signs, spatial structures, etc. Furthermore, there are methods based on laser radar point clouds and radar features.

[0006] For example, DE 10 2016 203 723 A1 discloses a method in which a vehicle determines its pose, i.e. its position and / or spatial orientation, by means of surroundings information and ascertains additional information about dynamic objects in the surroundings by means of surroundings sensors and uses it for determining the pose.

[0007] In addition, DE 10 2013 003 117 A1 discloses a method for self-localization of a vehicle, in which images of the surroundings are detected and analyzed by means of an image detection unit in order to compare image features with comparison features stored in a database in order to thereby determine the position of the vehicle. SUMMARY

[0008] It is an object of the present application to further improve the determination of the own position of a motor vehicle.

[0009] This object is achieved by a method according to the application, by a system according to the application and by a motor vehicle according to the application. The features, advantages and advantageous design options of the method according to the application can be regarded as features, advantages and advantageous design options of the system according to the application or of the motor vehicle according to the application and vice versa. The features, advantages and advantageous design options of the system according to the application can be regarded as features, advantages and advantageous design options of the motor vehicle according to the application and vice versa. The application also comprises combinations of features of the described embodiments.

[0010] The application therefore relates (in a first aspect) to a method for determining the own position of a motor vehicle. The motor vehicle can be configured as a car, in particular as a passenger car and / or a lorry. Furthermore, the motor vehicle can be a motorcycle or a self-propelled working machine. In particular, the motor vehicle is configured to be highly automated, fully automated and / or autonomously operable or drivable according to SAE J3016 in one of the levels mentioned at the beginning, in particular starting from level 3.

[0011] In this method, a map dataset of the vehicle's surrounding environment is provided to the data processing unit. The data processing unit is, in particular, a computer device that may be constructed inside and / or outside the vehicle. The data processing unit or computer device is configured to process and / or further process the data provided by the data processing unit.

[0012] In this method, at least one environmental element in the surrounding environment is detected at least partially by means of a sensor device in the motor vehicle, and a sensor device dataset that at least partially characterizes the surrounding environment is provided to a data processing unit. This means that the motor vehicle has a sensor device comprising at least one sensor unit. The corresponding sensor unit has at least one sensor on its side, such as an ultrasonic sensor, laser sensor or laser scanner, lidar sensor, camera sensor, radar sensor, etc. Furthermore, the sensor device can be configured to include a data processing unit, at least a portion of the data processing unit located within the vehicle. In any case, the corresponding sensor unit and the data processing unit are coupled to each other in terms of data technology, such that the corresponding sensor values ​​of the sensor unit can be provided to the data processing unit in data form.

[0013] The surrounding environment of a motor vehicle should be understood as the environment of the motor vehicle, especially the nearby area, wherein the surrounding environment or the part of the environment relevant to the method is limited or defined by, for example, the sensor range of the sensor device. In other words, the nearby area is the surrounding environment or the part of the environment relevant to the method.

[0014] In this method, the sensor device therefore includes, detects, or senses at least one feature or element arranged in the surrounding environment or nearby area. This feature or element is referred to as an "environmental element." An environmental element, also known as an environmental feature, is, for example, a component or element of infrastructure, particularly building elements (walls, ceilings, columns, floors, ground markings, signs, doors / door frames, doors / door leaves, windows, etc.) and / or traffic infrastructure elements (lanes, lane markings, lane markings, intersections, bike lanes, sidewalks, traffic control zones, curbs, green belts, vegetation, roundabout facilities, signs, traffic lights, railroad tracks, fences, etc.). These examples should be understood as exemplary rather than exhaustive.

[0015] Sensor device datasets contain at least one piece of information about detected environmental elements, such as the relative position of the environmental elements with respect to motor vehicles. Map datasets are maps in digital or data form. In particular, map datasets are digital terrain maps suitable for navigation, for example, using a navigation system. Because map datasets contain data or information about traffic networks and other natural and man-made objects closely connected to the earth's surface, this means that information about environmental elements can be stored in map datasets.

[0016] Both the sensor device dataset and the map dataset are provided to the data processing unit, which then generates and provides its own location dataset from the sensor device dataset and the map dataset. This means that the data processing unit is configured to process or further process the data in the sensor device dataset and the map dataset to generate its own location dataset or self-location dataset and, for example, provide it to a navigation system.

[0017] The self-position dataset includes at least one piece of information regarding the geographic longitude and geographic latitude of the motor vehicle (also referred to as "this vehicle") at its current location on Earth. Furthermore, the self-position dataset may contain information about its altitude at standard zero. In particular, the self-position dataset also includes information about the vehicle's orientation, i.e., which compass direction (e.g., expressed in degrees) the direction of travel arranged parallel to the vehicle's longitudinal axis currently or at its current location points to. Additionally, for the self-position dataset, information about the tilt position of the vehicle's longitudinal axis, lateral axis, and / or vertical axis relative to the horizon may be considered, for example, to confirm whether the vehicle is on / on a slope.

[0018] To further improve the determination of the vehicle's own position, according to the present invention, a contextual dataset (or background dataset, i.e., Kontextdatensatz) is provided to the data processing unit, and the self-position dataset is subjected to a plausibility check by the data processing unit based on this contextual dataset. In this case, it is particularly configured that the data in the map dataset and the data in the sensor device dataset are digitally correlated. This is preferably achieved based on a trial-and-error method, which associates one or more pieces of information about at least one element of the surrounding environment with the data in the map dataset.

[0019] In short, the vehicle estimates its own location or position and generates a self-position dataset, for example, using a data processing unit. This self-position dataset is provided to a navigation system for further navigation or guidance of the vehicle. The contextual dataset represents contextual knowledge used to reduce estimation inaccuracies. This is because, to estimate its own position, for example, using a mathematical algorithm, there exists a large number of possible solutions or a solution space with such a large number of solutions. These solutions are, for example, a large number of location assumptions or a large number of possible self-position datasets. The contextual knowledge or contextual dataset is then used to improve the delimitation of possible solutions or possible self-positions of the vehicle, wherein the solution space is further constrained compared to what is known in the prior art. This results in more efficient or more accurate and more efficient or faster solutions for mathematical algorithms and therefore a more accurate and faster determination of the vehicle's self-position.

[0020] Therefore, the reasonableness check of the self-position dataset confirms whether the measured or determined self-position of the vehicle (i.e., the self-position dataset) is reasonable. If, during the reasonableness check, the data processing unit obtains, for example, the following result: the surrounding environmental elements detected by the sensor devices are generally impossible to exist at the current self-position of the vehicle, then, for example, it can be configured that the method—at least partially—is repeated or implemented again, for example, until the reasonableness check yields a positive result. This is particularly advantageous for highly automated, fully automated, and / or autonomous driving tasks, such as the fully automated and / or autonomous parking process of a vehicle. This is because the determination of the vehicle's self-position is limited, especially when the vehicle's surrounding environment or its vicinity is poorly mapped in the digital map dataset, and / or only a few surrounding environmental elements related to the vicinity of the vehicle are stored in the map dataset. Thus, for example, the sensor device dataset and the map dataset can only be correlated effectively with limited trial and error to generate the self-position dataset. Contextual datasets or contextual knowledge are involved here and are used in the data processing unit to check the reasonableness of the self-position dataset.

[0021] In another advantageous embodiment of this method, at least one motion information of the surrounding environment elements is added to the contextual dataset used for the plausibility check of the self-location dataset. In other words, the motion state of the surrounding environment element to be detected or currently detected is determined or confirmed. Thus, it can be confirmed in or for this method whether the surrounding environment element is dynamic (i.e., currently moving) or static (i.e., fixedly arranged or stationary). Therefore, the contextual dataset thus includes contextual knowledge, such as characterizing whether the currently detected or identified surrounding environment element in the vicinity of the vehicle by the vehicle's sensor equipment is a stationary infrastructure element (e.g., building walls, etc.) or a currently moving surrounding environment element (e.g., other traffic participants, such as pedestrians, other motor vehicles, etc.).

[0022] For the plausibility check of the self-location dataset, information such as whether another motor vehicle (located next to this vehicle relative to its longitudinal axis) is stationary (i.e., parked) or moving (i.e., traveling) can be used, for example, by means of the vehicle's sensor equipment. This allows differentiation as to whether the vehicle has passed a parking space at its current or current location where another vehicle is parked, or whether the vehicle is passing through an adjacent lane where another vehicle is traveling. This plausibility check is therefore particularly reliable, resulting in a particularly accurate self-location dataset, or a particularly efficient selection from location assumptions.

[0023] Alternatively or additionally, at least one distance information related to the surrounding environment elements can be added to the context dataset for plausibility checking. This distance related to the surrounding environment elements may, for example, include the structural height of the surrounding environment elements, such as the height between a fixed surface (on which the surrounding environment element is placed or fixed) and a protruding element (e.g., the upper edge) of the surrounding environment element. This distance (e.g., structural height) related to the surrounding environment elements is then compared to a comparison value during plausibility checking. If, for example, it is determined that the distance related to the surrounding environment elements and the comparison value deviate excessively from each other, the plausibility check yields a negative result. Conversely, if the distance related to the surrounding environment elements and the comparison value at least substantially correspond to each other, the plausibility check yields a positive result. In this case, the vehicle's own position is plausible.

[0024] This distance information enables a more efficient and / or more accurate determination of the vehicle's position.

[0025] Distance information can be added by measuring the distance between surrounding environmental elements and the vehicle or the vehicle itself. This significantly improves the efficiency and accuracy of determining the vehicle's own position. For example, if, when roughly determining the vehicle's own position (where, for example, there are more than one location assumption), especially based on a map dataset, the vehicle is so close to an infrastructure element that it is impossible to place another surrounding environmental element between the vehicle and the infrastructure element, but, for example, another infrastructure element is detected between the vehicle and the infrastructure element by means of a sensor device, then the reasonableness check yields a negative result. In other words, the reasonableness check yields the following result: the vehicle's own position or surrounding environmental elements and / or another surrounding environmental element has been incorrectly detected. Wherein—as already mentioned above—in the case of a negative reasonableness check result, for example, the vehicle's own position is re-determined, or another own position in the possible own position dataset is selected, thus preventing the incorrect or inaccurate determination of the vehicle's own position.

[0026] Alternatively or additionally, the distance between a surrounding element and at least one other surrounding element can be added to the distance information. This makes the determination of the vehicle's own position more reliable or safer. This is because adding contextual knowledge to the contextual dataset, for example, regarding whether another surrounding element might exist between a given surrounding element and another surrounding element, if the three exemplary surrounding elements are constructed and / or arranged in a typical proportion, allows for the exclusion, for example, of pedestrians stopping or moving between two surrounding elements that are too close together, when a pedestrian is about to pass between them, based on a reasonableness check.

[0027] It has been shown that it is further advantageous to add information about the arrangement of surrounding environmental elements to the contextual dataset for plausibility checks, based on which the likelihood is assessed whether the surrounding environmental elements are arranged as prescribed in the current surrounding environment or in the vicinity of the vehicle. In other words, the plausibility check then assesses or examines whether it is entirely possible, for example, that surrounding environmental elements detected by the vehicle's sensor equipment are indeed arranged in the vicinity of the vehicle. This is particularly advantageous in parking facilities, especially parking garages, especially when the entrance and exit of the parking garage are close to each other, typically only a few meters apart. This is because confirming and / or determining whether the vehicle is positioned at the entrance or exit of the parking garage is particularly difficult or complex when determining its own location. If the sensor equipment now identifies surrounding environmental elements, such as parking garage elements, that can only be found at the entrance of the parking garage, the hypothetical location indicating that the vehicle is positioned at the exit of the parking garage can be discarded.

[0028] In another advantageous embodiment of the method, the contextual dataset is configured to be at least partially generated or expanded by the vehicle's sensor devices. This means that the vehicle's sensor devices are configured to detect the vehicle's surrounding environment in such a way that surrounding environmental elements can be detected, probed, or sensed. In particular, the vehicle's sensor devices are configured to detect motion information of surrounding environmental elements and provide it, for example, to a data processing unit. Additionally, the vehicle's sensor devices can be configured to detect at least one distance, particularly the distance between surrounding environmental elements and the vehicle and / or the distance between a surrounding environmental element and at least one other surrounding environmental element, and provide it, for example, to a data processing unit. Furthermore, the vehicle's sensor devices can be configured to detect corresponding surrounding environmental elements such that the arrangement information of the surrounding environmental elements is provided, for example, to the data processing unit.

[0029] If a contextual dataset or contextual knowledge is generated and / or expanded using the vehicle's sensor equipment, a separate sensor equipment for performing the method can be eliminated. In this case, the vehicle's sensor equipment then fulfills at least one dual function: - first - the sensor equipment provides functionality for performing a method for determining the vehicle's own position, and - second - the sensor equipment provides functionality for the vehicle's driving operation, such as for at least one driver assistance system. The correspondingly constructed vehicle is then configured to be particularly lightweight or efficient in terms of mass, thereby enabling the vehicle to operate with particularly high fuel efficiency or energy efficiency and / or low emissions.

[0030] Finally, according to another embodiment of the method, the text of surrounding environmental elements is detected by a semantic processor unit of a sensor device, and its semantic content is added to a contextual dataset. For this purpose, the sensor device (especially the semantic processor unit) has a text recognition unit, which can be, for example, a traffic sign recognition unit of a motor vehicle. This means that in this method, the text of surrounding environmental elements (e.g., signs) is read and evaluated by the machine, and the corresponding evaluation result is added to the contextual dataset as further contextual knowledge. To try to associate the example of the motor vehicle with the parking garage, for example, the motor vehicle (especially its sensor device) can be configured to detect and evaluate the text "entrance," thereby using this text to check the plausibility of its own location dataset. Therefore, if a rough self-location determination, for example based on a map dataset, indicates that the vehicle is located at the entrance of the parking garage, then the vehicle's self-location dataset is evaluated as plausible. This further improves the accuracy in determining the motor vehicle's own location.

[0031] This invention is based on the idea that simply associating observed features with map features is not always unique and robust. For example, repetitive structures in the parking environment (e.g., in a parking garage) can lead to the incorrect association of surrounding elements. This is, for instance, when a vehicle detects a large number of identical or at least similar pillars, wall elements, signs, etc., and may incorrectly associate them with one of the possible location assumptions or one of the possible self-location datasets. The vehicle's self-location determination will then be incorrect or at least inaccurate. However, some non-uniqueness can be addressed if, in the method—for example, according to the present invention—contextual knowledge, such as about dynamic objects and / or distances, is used in addition to detecting surrounding elements. For example, an observed or detected ground line (Bodenlinie) by means of a sensor device cannot be directly located at a wall if a dynamic object is observed behind the ground line in the method, or if, for example, an ultrasonic sensor indicates a free space larger than physically possible. In other words, the method effectively prevents the ground line detected by the vehicle's sensor device from being (erroneously) interpreted as a ground line directly located at a wall.

[0032] This method also involves a particularly accurate approach for self-localization or self-position determination. This means that the self-position dataset is particularly accurate, especially in determining the self-position of a vehicle within centimeters. In this context, it is also referred to as so-called HD-localization (“high-definition”). In this context, it should be understood that identifying dynamic objects is not used, for example, to estimate the presence of bicycle paths or to utilize that information when comparing them with digital maps or map datasets. Instead, information about the presence of objects at certain locations in the environment surrounding the vehicle is utilized without object classification and / or without assuming conditions such as lanes or bike paths. This is used to more effectively constrain the solution space of the optimization problem (estimation of the self-position of the vehicle and / or selection from more than one possible location assumption). Another example of this is, for instance, detecting a pedestrian or other human being beside the vehicle using the vehicle's sensor equipment, thereby generating additional (mathematical) conditions for estimating the self-position or for the optimization problem. These additional conditions indicate that the self-position cannot be directly located at a wall, because pedestrians would not pass between the wall and the vehicle.

[0033] Furthermore (in a second aspect), the present invention relates to a system for determining the position of a motor vehicle, wherein the system is configured to perform the method. The second aspect of the invention also includes improvements to the system according to the invention, which have features as described in conjunction with the features of the method according to the invention. For this reason, corresponding improvements to the system according to the invention will not be described herein.

[0034] Finally, the present invention (in a third aspect) relates to a motor vehicle equipped with a system for determining the position of the motor vehicle itself. The third aspect of the invention also includes improvements to the motor vehicle according to the invention, which have features, as described in conjunction with features of the method and / or system according to the invention. For this reason, corresponding improvements to the motor vehicle according to the invention will not be described here. Attached Figure Description

[0035] Embodiments of the present invention are described below. Regarding this:

[0036] Figure 1 A schematic diagram of a motor vehicle is shown, which has a system for determining its own position.

[0037] Figure 2 A schematic top view of the vehicle in a traffic situation is shown;

[0038] Figure 3 A schematic top view of the vehicle in another traffic situation is shown; and

[0039] Figure 4A schematic top view of the vehicle in another traffic situation is shown. Detailed Implementation

[0040] The embodiments described below are preferred embodiments of the present invention. In these embodiments, the components described are various features of the present invention that can be examined independently of each other, and these features also independently improve the present invention and are thus considered as part of the present invention individually or in combinations other than those shown. Furthermore, the described embodiments can be supplemented by other features of the features already described in the present invention.

[0041] In these accompanying drawings, elements with the same function are given the same reference numerals.

[0042] The following describes together a method and system 1 for determining the position 2 of a motor vehicle 3, and a motor vehicle 3 equipped with the system 1.

[0043] Figure 1 The diagram illustrates a motor vehicle 3 equipped with a system 1 for determining its own position 2. System 1 is configured to implement a method for determining the own position 2 of the motor vehicle 3. Therefore, system 1 has devices that can be used or are used to perform this method.

[0044] Thus, system 1 or the vehicle 3 equipped with system 1 has a data processing unit 4 and sensor devices 5. The data processing unit 4 is, in particular, a computer device, such as a vehicle navigation system (not shown in detail) or, especially, a satellite navigation system (within the vehicle) computing unit 6. Alternatively or additionally, the computer device or data processing unit may be part of a server device 7, which is particularly configured as an (external to the vehicle) server device 7, such as a cloud server device. In this case, the vehicle 3 and the server device 7 each have a corresponding data transceiver 8 for wireless data communication.

[0045] In the current example, vehicle 3 is constructed as a passenger car. However, it is equally conceivable that vehicle 3 could be constructed as a truck, motorcycle, or self-propelled work machine. Vehicle 3 or passenger car has a steering and drive unit 9 that provides at least one highly automated, fully automated, and / or autonomous operating mode for vehicle 3. This means that vehicle 3 or passenger car is constructed to drive in a highly automated, fully automated, and / or autonomous manner. This allows, for example, the driver of vehicle 3 to refrain from the corresponding driving task at least temporarily and / or in certain preset or specific application situations of vehicle 3 if vehicle 3, in particular steering and drive unit 9, is constructed according to Level 3 of SAE J3016. If steering and drive unit 9 is constructed according to Level 4 of SAE J3016, then at least one fully automated operating mode is provided for vehicle 3. Thus, the driver of vehicle 3 is allowed, at least temporarily (e.g., for the entire duration of a specific application situation), to completely relinquish the completion of the driving task to vehicle 3—the driver is then merely a passenger of vehicle 3. If the steering and drive unit 9 or the vehicle 3 is constructed according to Level 5 of SAE J3016, then at least one autonomous operation mode of the vehicle 3 is realized. In this case, there is no longer any human driver to intervene in the driving operation of the vehicle 3. This means that the vehicle 3, which is autonomously driven according to Level 5, is used to transport only passengers (and possibly goods), where the passengers do not control or supervise / monitor the driving task of the vehicle 3 at any time.

[0046] In modern motor vehicles, such as vehicle 3, starting from level 3, it is configured such that, for example, after the driver has activated or triggered the corresponding function, vehicle 3, especially its steering and drive unit 9, performs or completes at least one driving task without the driver's active assistance. A prominent example of this is, for example, parking steering assist of vehicle 3, enabling vehicle 3 to park and / or exit parking spaces in a highly automated, fully automated, and / or autonomous manner.

[0047] In order to implement highly automated, fully automated, and / or autonomous driving tasks with exceptional safety, the steering and drive unit 9 of the vehicle 3 needs to be provided with a particularly accurate determination of the vehicle 3's own position 2. In short, the vehicle must "know" where it is at the start of the highly automated, fully automated, and / or autonomous driving task and during that time.

[0048] For this purpose, a map dataset 10 is provided to the data processing unit 4, in which the surrounding environment 11 of the vehicle 3 is stored (e.g., stored) in digital form (i.e., in data form). Then, for example, it can be configured such that, in the method of determining the vehicle 3's own position 2 based on the map dataset 10 (where it is, for example, a topographic map), a rough determination of its own position is first performed.

[0049] Furthermore, in this method, at least one ambient element 12 is detected at least partially by means of a sensor device 5 of the vehicle 3. For this purpose, the sensor device 5 is configured to generate a sensor device dataset 13 or a dataset derived from the sensor device 5 based on the corresponding detection results, which at least partially characterizes the ambient environment 11, particularly at least one ambient element 12. Additionally, the sensor device 5 is configured to provide the sensor device dataset 13 to the data processing unit 4, for example, by sending it to the data processing unit 4 or by providing it for download by the data processing unit 4.

[0050] In a further step of the method, the data processing unit 4 combines the map dataset 10 and the sensor device dataset 13 such that the data processing unit 4 generates a self-position dataset 14 from the sensor device dataset 13 and the map dataset 10, and provides (e.g., delivers) it to the vehicle navigation system and / or the steering and drive unit 9, for example. This self-position is determined based on or according to a rough self-position determination, for example, the existence of multiple location assumptions or multiple self-position datasets, each representing a possible self-position 2 of the vehicle 3.

[0051] Especially in driving tasks, such as driving maneuvers requiring particularly high trajectory accuracy of vehicle 3, particularly when parking and / or exiting parking spaces, there is a need for particularly accurate and rapid determination of its own position 2. To this end, a contextual dataset 15 is provided to the data processing unit 4, based on which the self-position dataset 14 undergoes a plausibility check. Thus, for example, if the data processing unit 4 has determined or estimated two or more location hypotheses that are equivalent to each other and that are based on a trial-and-error evaluation or combination of sensor device dataset 13 and map dataset 10, the plausibility of the corresponding location hypotheses is checked using the contextual dataset 15. Then, based on the plausibility check, at least one of the possible location hypotheses for self-position 2 is eliminated if the location hypothesis fails the plausibility check. Therefore, when estimating the self-position of vehicle 3, the number of possible solutions is particularly small, thus making the estimation or determination of the self-position of vehicle 3 more efficient, because ultimately it is selected from fewer location hypotheses.

[0052] Figure 2 The traffic situation of motor vehicle 3 is shown in a schematic top view, in which the position 2 used to determine or ascertain its own location is shown. Figure 1 In the method, the context dataset 15 (see...) Figure 1At least one motion information 16 of the corresponding surrounding environment element 12 is added for plausibility checks. The final list of surrounding environment elements 12 considered or understood in this method will exceed the scope of this specification. Therefore, only a few types of surrounding environment elements 12 are discussed, described as representing all conceivable surrounding environment elements 12. In particular, each object in the surrounding environment 11 of the motor vehicle 3 can be a surrounding environment element 12 if the corresponding object can be sensed or detected by means of sensor device 5. In the accompanying drawings, not all conceivable surrounding environment elements 12 are given their own corresponding reference numerals to ensure particularly good visual clarity.

[0053] according to Figure 2 The corresponding surrounding environmental element 12 could be, for example, another motor vehicle 17. Another example of at least one surrounding environmental element 12 is lane markings 18, vegetation—especially trees 19, traffic signs 20, other traffic participants, such as pedestrians 21, etc.

[0054] In the method for determining the position 2 of the motor vehicle 3, motion information 16 is used to determine whether the corresponding surrounding environmental element 12 is currently moving or whether the corresponding surrounding environmental element 12 is currently stationary.

[0055] exist Figure 2 The text also shows that, given the context dataset 15 (see...), Figure 1 Add at least one distance information for distance 22 related to the corresponding surrounding environmental element 12 for plausibility checks. (Refer to again) Figure 1 The corresponding surrounding environmental element 12 is shown as a traffic sign 20, and the distance 22 can be, for example, the height 23 of the surrounding environmental element 12 or the traffic sign 20. Furthermore, the distance information can include a distance quantity 24, which characterizes the spatial distance between the corresponding surrounding environmental element 12 and the vehicle 3. It is conceivable that the corresponding distance quantity 24 is measured or measured parallel to the longitudinal axis of the vehicle 3 or parallel to the lateral axis of the vehicle 3. Alternatively, it is conceivable to directly measure the distance between the vehicle 3 and the corresponding surrounding environmental element 12. Figure 2 In the figure, only a few of the distances 24 or 22 under consideration are shown.

[0056] Figure 3 A schematic top-down view illustrates another traffic situation involving vehicle 3, where vehicle 3 is positioned on and traveling specifically on one of multiple lanes 25. Furthermore, in Figure 3 The safety lane strip 25a is drawn, which is commonly referred to as the "emergency lane strip". The safety lane strip 25a or emergency lane strip can also be an environmental element 12. Similarly, the corresponding lane 25 or the corresponding lane strip can form the corresponding environmental element 12. Figure 3A few of the distance quantities 24 are drawn, representing the respective distances between vehicle 3 and the corresponding surrounding environmental elements 12, especially the corresponding other vehicles 17. Furthermore, Figure 3 Several other distance quantities 26 are drawn, which represent the spatial distance between the corresponding surrounding environmental element 12 and at least one other surrounding environmental element 12.

[0057] Distance information, namely distance 24 and / or distance 26, is provided (e.g., by means of data processing unit 4 (see...) Figure 1 It was added to the context dataset 15 (see) Figure 1 This is used to perform a plausibility check on the self-position dataset 14. The corresponding measurements of distances 24 and 26 and / or distance 22, especially height 23, are preferably performed using the corresponding sensors of sensor device 5.

[0058] Furthermore, in such a situation Figure 3 In the traffic conditions shown, the plausibility check of the self-position dataset 14 can use the corresponding motion information 16 of other motor vehicles 17 or at least one of the motor vehicles 17. In this case, the sensor device 5 is configured to detect the motion information 16 of the corresponding motor vehicle 17 or the corresponding surrounding environmental element 12, for example by means of radar sensors and / or laser scanners.

[0059] Figure 4 Another traffic situation for motor vehicle 3 is shown in a schematic top view. Figure 4 The diagram shows a motor vehicle 3, which travels, for example, on parking level 27, particularly on a multi-level parking garage 28, and especially on a fully automated, highly automated, and / or autonomous basis, for example, based on the operation of a parking steering assist system for the motor vehicle 3. For this purpose, wall elements 29 of the parking garage 28 are shown in cross-section. Thus, a plan view of the ground of parking level 27 is obtained, in which the motor vehicle 3 is positioned as prescribed on the ground, and particularly on which it rolls or travels. Figure 4Other examples of corresponding environmental elements 12 are illustrated, such as a corresponding one of the wall elements 29. Furthermore, an opening 30 in one of the wall elements 29 or between two wall elements 29 can form one of the environmental elements 12. The opening 30 can be, for example, a window opening, a door opening, a gate opening, etc. Especially in parking garages, for safety reasons, restricted areas 31 are often arranged in front of the opening 30 on the floor of the parking level 27, through which pedestrians or walkers can access the parking level 27. Such restricted areas 31 can also be environmental elements 12. Furthermore, such restricted areas 31 can be arranged in many other places, especially in public transportation areas, such as before and after public transportation stops, at intersections / inside, for lane blocking, etc.

[0060] by Figure 4 Taking traffic conditions as an example, this describes adding arrangement information of at least one of the surrounding environmental elements 12 within the range of sensor device 5 to the context dataset 15 for reasonableness checking. Based on this arrangement information—especially with the aid of data processing unit 4—the possibility that the corresponding surrounding environmental element 12 is arranged as required in the current surrounding environment 11 of the motor vehicle 3 is evaluated. Figure 2 Observe together Figure 4 Here, for example, it is obvious that if sensor device 5 detects or identifies an environmental element 12 that is inappropriate in the context of parking garage 28 or parking level 27, then the reasonableness check of the assumption that the vehicle 3 is located in parking garage 28 or on parking level 27 yields a negative reasonableness check result. It is particularly noteworthy that if... Figure 4 If a tree 19 is detected in a motor vehicle 3 on parking level 27 or in parking garage 28, as shown, the reasonableness check will deliver a negative reasonableness check result.

[0061] The plausibility check is more effective if the data processing unit is provided with the semantic content of one or more surrounding environmental elements 12. For this purpose, the vehicle 3 (especially its sensor device 5) can be configured to have a semantic processor unit that is designed to detect the text of the surrounding environmental elements 12 and provide the semantic content of the text. For example, if the text "Exit" is placed on the directional sign 32, the correspondingly different location assumption (i.e., the vehicle 3 will be in the area of ​​the entrance to the parking garage 28) can be discarded, because this (incorrect) location assumption will fail at the latest during the plausibility check.

[0062] List of reference numerals in the attached diagram:

[0063] 1 System

[0064] 2. Own position

[0065] 3 Motor vehicles

[0066] 4 Data Processing Unit

[0067] 5. Sensor devices

[0068] 6. Calculation Unit

[0069] 7 server devices

[0070] 8 Data transceivers

[0071] 9. Steering and Drive Unit

[0072] 10 Map Datasets

[0073] 11 Surrounding Environment

[0074] 12 Surrounding environmental elements

[0075] 13 Sensor Device Datasets

[0076] 14 Self-location dataset

[0077] 15 Context Datasets

[0078] 16 Sports Information

[0079] 17 Motor vehicles

[0080] 18 lane markings

[0081] 19 Trees

[0082] 20 Traffic signs

[0083] 21 pedestrians

[0084] 22 Distance

[0085] 23 Height

[0086] 24 Distance Measurement

[0087] 25 lanes

[0088] 25a Safety belt

[0089] 26 Distance Measurement

[0090] 27 parking levels

[0091] 28 Parking Garage

[0092] 29 Wall Elements

[0093] 30 Opening

[0094] 31. Restricted Area

[0095] 32 Directional Signs

Claims

1. A method for determining the position (2) of a motor vehicle (3), wherein, A map dataset (10) of the surrounding environment (11) of the motor vehicle (3) is provided to the data processing unit (4), and at least one surrounding environment element (12) in the surrounding environment (11) is detected at least partially by means of the sensor device (5) of the motor vehicle (3), and a sensor device dataset (13) that at least partially characterizes the surrounding environment (11) is provided to the data processing unit (4), thereby enabling the data processing unit (4) to generate and provide its own location dataset (14) from the sensor device dataset (13) and the map dataset (10). Its features are, A contextual dataset (15) is provided to the data processing unit (4), and the self-position dataset (14) is subjected to a rationality check by means of the data processing unit (4) based on the contextual dataset, wherein at least one motion information (16) of the surrounding environment element (12) is added to the contextual dataset (15) for rationality check.

2. The method according to claim 1, characterized in that, At least one distance information of the distance (22) associated with the surrounding environmental element (12) is added to the context dataset (15) for reasonableness checking.

3. The method according to claim 2, characterized in that, The distance (24) between the surrounding environmental element (12) and the motor vehicle (3) is added to the distance information.

4. The method according to claim 2 or 3, characterized in that, The distance (26) between the surrounding environment element (12) and at least one other surrounding environment element (12) is added to the distance information.

5. The method according to any one of the preceding claims, characterized in that, The arrangement information of the surrounding environmental elements (12) is added to the context dataset (15) for reasonableness checking, and the following possibility is evaluated based on the arrangement information: whether the surrounding environmental elements (12) are arranged in accordance with the provisions in the current surrounding environment (11) of the motor vehicle (3).

6. The method according to any one of the preceding claims, characterized in that, The contextual dataset (15) is generated or expanded at least in part by the sensor device (5) of the motor vehicle (3).

7. The method according to claim 6, characterized in that, The semantic processor unit of the sensor device (5) detects the text of the surrounding environment element (12) and adds its semantic content to the context dataset (15).

8. A system (1) for determining the position (2) of a motor vehicle (3) by means of a method constructed according to any one of the preceding claims.

9. A motor vehicle (3) having a system (1) constructed according to claim 8 for determining the position (2) of the motor vehicle (3).

Images