Method for satellite-assisted determination of vehicle position

Abstract

The present invention relates to a method for satellite-assisted determination of a vehicle position, comprising the following steps: a) receiving GNSS satellite data, b) determining the vehicle position using the GNSS satellite data received in step a), c) providing input variables that may affect the accuracy of the vehicle position determined in step b), d) determining the position accuracy of the vehicle position determined in step b) using an algorithm that assigns a position accuracy to the vehicle position, and e) adjusting the algorithm.

Description

Technical Field

[0001] The present invention relates to a method for satellite-supported determination of a vehicle position, a method for improving the accuracy estimation of satellite-supported determination of a vehicle position, a computer program, a machine-readable storage medium, and a motion and position sensor. The present invention is particularly suitable for use in autonomous driving. Background Art

[0002] An autonomous vehicle is a vehicle without driver management. The vehicle drives autonomously, for example, by independently identifying the road direction, other traffic participants or obstacles and calculating the corresponding control commands in the vehicle and forwarding them to the actuators in the vehicle, thereby correctly influencing the driving route of the vehicle. The driver does not participate in the driving activity in a fully autonomous vehicle.

[0003] Currently available vehicles are not yet capable of autonomous operation. First, because the technology has not been fully developed. On the other hand, because today's laws still require that the driver must be able to intervene in the driving activity at any time. This complicates the implementation of autonomous vehicles. However, there are already systems from various manufacturers that demonstrate autonomous or semi-autonomous driving. These systems are undergoing intensive testing. Even today, it is foreseeable that once the above-mentioned obstacles are removed, fully autonomous vehicle systems will enter the market within a few years.

[0004] In addition, vehicles for autonomous operation require a sensor system that can determine the vehicle position with high precision, in particular via navigation satellite data (GPS, GLONASS, Beidou, Galileo). For this purpose, GNSS (Global Navigation Satellite System) signals are currently received via a GNSS antenna on the vehicle roof and processed by a GNSS sensor. In addition, GNSS correction data can be considered to improve the positioning result. A particularly advantageous GNSS sensor is the so-called motion and position sensor, which can use GNSS data to determine at least one vehicle position or vehicle orientation or vehicle motion.

[0005] With a motion and position sensor, the position of one's own vehicle can be determined with high accuracy. In addition to GNSS data, GNSS correction data, wheel speed, and vehicle steering angle are used in the motion and position sensor to determine the vehicle position with high accuracy. The individual input data are fused into the (total) vehicle position within a Kalman filter. The result is the estimated (own) position of the vehicle in the world, for example, in GNSS coordinates.

[0006] As described above, motion and position sensors are used to determine the combined vehicle position (GNSS, GNSS correction data, wheel speed, steering angle, acceleration data, rotation rate data, etc.). However, this highly accurate vehicle position from motion and position sensors is affected by accuracy fluctuations. Therefore, it is desirable to also achieve a statistical description of the currently available position accuracy by the motion and position sensors. This statistical description is the statistical position accuracy, which is also known as the so-called protection limit (PL) in the professional field. For example, the position deviation of the vehicle may not be greater than a determined number of meters in the travel direction, not greater than a determined number of meters laterally, and not greater than a determined number of meters in height. When the protection limit is exceeded, a warning is output, for example, by the motion and position sensors. Other control devices of the vehicle accessing the motion and position sensor positions can use this information to allow or interrupt further processing of the motion and position sensor data.

[0007] For this purpose, the protection limit must be determined as accurately as possible statistically. For example, the currently available position accuracy in current motion and position sensors is determined based on the currently dominant scenario in the vehicle and a look-up table. This look-up table is calibrated using as many scenarios of existing parameters as possible. For this purpose, a large number of driving scenarios are required, which include, for example, different driving trajectories or different GNSS satellite positioning distribution data. Therefore, it is not possible in practice to fully parameterize such a look-up table for all scenario parameters present in the vehicle. For example, every conceivable driving scenario would have to be recorded once at every location in the world at every time, and the statistical parameters for the position description from the motion and position sensors would be used according to different scenarios. This is not possible in practice because, for example, the number of scenarios during test drives is limited. In addition, the number of vehicles for this data recording is also limited. Summary of the Invention

[0008] Here, according to the present invention, a method for satellite-supported determination of a vehicle position is proposed, comprising the following steps:

[0009] a) Receiving GNSS satellite data,

[0010] b) Determining the vehicle position using the GNSS satellite data received in step a),

[0011] c) Providing input variables that may affect the accuracy of the vehicle position determined in step b),

[0012] d) Determining the position accuracy of the vehicle position determined in step b) using an algorithm that assigns position accuracy to the vehicle position,

[0013] e) Adjusting the algorithm.

[0014] This method is particularly used for satellite-supported determination of the vehicle position by means of one or more motion and position sensors of the vehicle itself. The vehicle itself is preferably an autonomous vehicle, in particular an autonomously operating motor vehicle. GNSS stands for Global Navigation Satellite System. GNSS is a system for position determination and / or navigation on the earth and / or in the air by receiving signals from navigation satellites, which signals are hereinafter referred to as satellite data. The Global Navigation Satellite System is herein the collective term for existing and future global satellite systems, such as GPS (NAVSTRAR GPS), GLONASS, Beidou and Galileo. Accordingly, a GNSS sensor is a sensor suitable for receiving and processing, for example evaluating, navigation satellite data. Preferably, the GNSS sensor is capable of determining the vehicle position with high precision using navigation satellite data (GPS, GLONASS, Beidou, Galileo). In particular, GNSS data are the data received from navigation satellites, and GNSS data may also be referred to as "navigation satellite data".

[0015] In step a), reception of GNSS satellite data is effected. Preferably, the motion and position sensors of the vehicle itself receive GNSS satellite data from at least one GNSS receiving unit (e.g. in particular a GNSS antenna on the vehicle), and the GNSS antenna in turn communicates directly with the navigation satellites or receives satellite signals. In step b), the vehicle position of the vehicle itself is determined using the GNSS satellite data received in step a). In step b), the vehicle position of the vehicle itself is preferably determined by the motion and position sensors of the vehicle itself. At least in step a) or b), it is possible to effect the transit time measurement of the navigation satellite signals.

[0016] In step c), provision of input variables is effected, which input variables can influence the accuracy of the vehicle position determined in step b). In addition, one or more further variables or data are herein available as input variables:

[0017] · The date and time at the vehicle position,

[0018] · The satellite positioning distribution (ephemeris data) or the number of available satellites with satellite numbers at this position,

[0019] · The signal strength or Carrier-to-Noise Ratio of the available satellites at this position and at this time in this scenario,

[0020] · Data from the vehicle's environment sensors, which data gives information about additional structures around the vehicle or traffic participants around the vehicle (e.g. whether a truck is around the vehicle itself, which truck for example just obscures a particular satellite),

[0021] · Position data of other traffic participants,

[0022] · Data of other traffic participants, such as their length, width and / or height,

[0023] · The speed of other traffic participants, especially the speed relative to the vehicle itself,

[0024] · The speed of the vehicle itself,

[0025] · Structures or functions such as infrastructure points (buildings, signs, traffic lights) around the vehicle itself,

[0026] · The instantaneous acceleration and / or rotational speed of the vehicle itself,

[0027] · The wheel speed and / or rotational direction of the vehicle,

[0028] · The steering angle of the vehicle itself,

[0029] · The availability and / or data content of at least one GNSS correction service (via L-band or vehicle-to-X communication connection).

[0030] In particular, the above input variables are not exhaustive. It is obvious here that a look-up table that wants to use all the mentioned input variables requires a large amount of memory in the vehicle (itself) or in the motion and position sensors. Even with all possible scenarios, the look-up table will never be complete in practice. It has surprisingly been found that an algorithm, especially a learning algorithm for replacing the previously used look-up table, is very beneficial because on the one hand it can save storage space and, if necessary, can also cover all possible scenarios more comprehensively than the look-up table.

[0031] In step d), an algorithm that assigns a position accuracy to the (determined) vehicle position is used to determine the (statistical) position accuracy of the vehicle position determined in step b). The algorithm preferably takes into account at least one input variable to assign a position accuracy to the (determined) vehicle position. Particularly preferably, the algorithm determines or calculates the (statistical) position accuracy of the vehicle position determined in step b) based on the vehicle position determined in step b) and at least one input variable provided in step c). For example, the algorithm outputs a deviation such as + / - 1 meter or, for example, 2% (relative to the vehicle position) as an output value if it is provided with the determined vehicle position and the time (date and time) at which the vehicle position was determined as input values. In other words, this example especially means that a certain inaccuracy that can be expected at a certain determined position at a certain specific time is stored in the algorithm (in a functional relationship). In other words, in the example described here, the algorithm describes the position accuracy as a function of the determined vehicle position and time.

[0032] Between steps d) and e), the position accuracy measured in step d) can be assigned to the vehicle position measured in step b). This advantageously allows components, such as a control device for the autonomous driving of a vehicle, to decide whether and how to use this vehicle position, where the control device is provided here with, for example, a pair of values generated by the assignment.

[0033] In step e), an adjustment of the algorithm is implemented. This is used in particular for determining a protection limit. The adjustment of the algorithm is preferably implemented taking into account the input variable provided in step c) or at least one of the input variables provided in step c) and / or taking into account a reference position or a comparison between the measured vehicle position and a relevant reference position. Returning to the above example, for instance, if it is recognized based on a comparison between the measured vehicle position and the reference position that there is a deviation of, for example, + / - 1 meter or, for example, 2% (relative to the actual measurement position) at the measured vehicle position at a specific time, then the algorithm for determining the protection limit can be adjusted so that it can map this relationship.

[0034] The algorithm is preferably a so-called learning algorithm. The adjustment of the algorithm is preferably carried out during or after a learning phase. A particular aspect of the solution presented here can be seen in that the lookup tables used hitherto have been replaced by an algorithm, in particular a learning algorithm. The algorithm is in particular configured to calculate the statistical accuracy (protection limit) of the vehicle position available or measured by motion and position sensors. In other words, this particularly means that the algorithm gives a functional relationship of the position accuracy of the motion and position sensors.

[0035] According to an advantageous design, steps a) to e) are carried out on the vehicle side. In other words, this particularly means that all steps a) to e) are carried out by the sensors and / or control devices of the vehicle itself. For example, the adjustment according to step e) can be implemented during a learning phase. It can also be proposed that the algorithm is further developed or adjusted outside the vehicle. In this case, the adjustment according to step e) can be represented, for example, by replacing or updating the algorithm stored on the vehicle side with an algorithm further developed or adjusted outside the vehicle, or by implementing the fusion of these algorithms.

[0036] Preferably, the algorithm used in step d) for measuring the position accuracy is provided even before the first adjustment in step e). This algorithm can be called the "initial" algorithm. The algorithm can be stored, for example, in a control device or a memory.

[0037] According to another advantageous design, at least step e) is carried out outside the vehicle, and at least one piece of information for adjusting the algorithm stored on the vehicle side is provided to at least one vehicle. For this purpose, step e) can be carried out by a central processing unit, which can receive data and / or algorithms from a plurality of vehicles. Furthermore, it is advantageous that the algorithm adjusted outside the vehicle can be used for a large number of vehicles.

[0038] According to an advantageous design, the algorithm determines the position accuracy based on the (measured or GNSS-based) vehicle position and at least one input variable. Particularly preferably, the algorithm determines or calculates the (statistical) position accuracy of the vehicle position measured in step b) based on at least one of the vehicle position measured in step b) and the input variable provided in step c). Returning to the above example, the algorithm can output, for example, a deviation of + / - 1 meter or, for example, 2% (relative to the actual vehicle position) as an output value if it is provided with the measured vehicle position and the time (date and time) at which the vehicle position was measured as input values.

[0039] According to another aspect, a method for improving the accuracy estimation of satellite-supported vehicle position determination is provided, including the following steps:

[0040] i) Detect the GNSS-based vehicle position,

[0041] ii) Receive input variables that may affect the accuracy of the vehicle position detected in step i),

[0042] iii) Detect the reference position of the vehicle position detected in step i),

[0043] iv) Adjust the algorithm that assigns position accuracy to the vehicle position by considering at least one comparison between the vehicle position detected in step i) and the reference position detected in step iii) or by considering at least one of the input variables received in step ii).

[0044] The method is preferably used to automatically determine the so-called protection limits in motion and position sensors. In addition, the method preferably helps to provide a (learning) algorithm that can advantageously replace the lookup tables used so far (by functional relationships).

[0045] In step i), the detection of the GNSS-based vehicle position is implemented. For this purpose, the GNSS-based vehicle position can be detected, for example, by means of the motion and position sensors of the vehicle. In addition, the (detected) GNSS-based vehicle position can be received, for example, from the control device of the vehicle. Alternatively, the (detected) GNSS-based vehicle position can be received by a central processing unit (outside the vehicle).

[0046] In step ii), the reception of input parameters is implemented, which affect the accuracy of the vehicle position detected in step i). In terms of the input parameters, reference is made to the input variables mentioned above in connection with the method for satellite-supported determination of the vehicle position. The input variables can be provided, for example, by the corresponding sensors of the (own) vehicle. In addition, the input variables can be received, for example, via the vehicle's control device and / or motion and position sensors. Additionally, the input variables can be received by a central processing unit (external to the vehicle).

[0047] In step iii), a reference position is detected for the vehicle position detected in step i). The reference position is generally a (highly accurate) vehicle position from an alternative positioning system (relative to the motion and position sensors of the own vehicle). The alternative positioning system is particularly located within the own vehicle.

[0048] For example, the reference position can be determined, for example, by the vehicle's position on a (digital) map (so-called Feature Map) with the aid of a map control device, with the aid of environmental sensor data, or with the aid of the transit time of the vehicle-to-X communication signal. In particular, the reference position relates to the actual or a more accurate vehicle position than that determined at a point in time (based on GNSS or using motion and position sensors) at which the detected or GNSS-based vehicle position is detected. For example, the reference position can be received via the vehicle's control device and / or motion and position sensors. Additionally, the reference position can be received by a central processing device (external to the vehicle).

[0049] Car-to-car communication (English: Car-to-Car communication, or abbreviated Car2Car or C2C) should be understood as the exchange of information and data between (motor) vehicles. The purpose of this data exchange is to inform the driver in advance of serious and dangerous situations. The vehicles in question collect data, such as ABS intervention, steering angle, position, direction, and speed, and send this data to other road users via radio (WLAN, UMTS, etc.). Here, the "driver's field of vision" should be extended electronically. Car-to-infrastructure (English: Car-to-Infratructure or abbreviated C2I) should be understood as the data exchange between a vehicle and the surrounding infrastructure (such as traffic lights). The technologies mentioned are based on the interaction of sensors of different road users and use the latest communication technology methods to exchange this information. Vehicle-to-X is a general term for various communication connections, such as vehicle-to-vehicle and vehicle-to-infrastructure.

[0050] In step iv), the algorithm that assigns a position accuracy to the (measured or GNSS-based) vehicle position is adjusted taking into account at least one comparison between the vehicle position detected in step i) and the reference position detected in step iii) or taking into account at least one of the input parameters received in step ii). The input variables are generally variables that are present and / or determined or measured at a point in time at which the GNSS-based vehicle position and / or the reference position is determined. In other words, the input parameters, the (measured or GNSS-based) vehicle position, and the reference position generally have the same time stamp. The algorithm is preferably adjusted based on a comparison between the (measured or GNSS-based) vehicle position and the reference position and at least one input variable. This comparison generally provides a position deviation or position accuracy of the (motion and position sensors) at the relevant vehicle position. In other words, this particularly means that the comparison describes the instantaneous position deviation of the motion and position sensors relative to the reference system. The position deviation is generally caused by at least one input variable. Returning to the example above, at a specific point in time (input variable), a position deviation can occur at a specific vehicle position, for example due to a time perturbation and / or interruption of the GNSS signal. This deviation can be determined by comparing the vehicle position with the reference position at that time. The algorithm can then be adjusted to map this relationship.

[0051] According to an advantageous design, steps i) to iv) are carried out outside the vehicle. Steps i) to iv) are preferably carried out by a central and / or superior processing device. The (one or more) vehicle positions, the (one or more) reference positions, and the input variables can be transmitted to the processing device, for example, via a radio connection, in particular a vehicle-to-X communication connection. In addition, the adjusted algorithm can be transmitted from the processing device to the (own) vehicle and / or multiple vehicles, for example, via a radio connection, in particular a vehicle-to-X communication connection. The processing device can be formed in the manner of a so-called cloud or in the manner of a so-called HIL (Hardware in Loop) system.

[0052] Preferably, the input data together with the instantaneous position deviation of the motion and position sensors relative to the reference system are transmitted to a cloud or a HIL (Hardware in Loop) system (e.g., by means of a vehicle-to-X communication connection). The adjustment (improvement) of the algorithm is now preferably achieved in the cloud or in the HIL by the input data or the position deviation from at least one vehicle. However, it is advantageous that the input data or the position deviation of other vehicles can also be used. The cloud or the HIL generally has a significantly higher computing power than the motion and position sensors. In this way, the algorithm for illustrating (protection limit) position accuracy can be adjusted (improved) in a preferred manner as quickly as possible and in particular be transmitted back to the vehicle equipped with the motion and position sensors via the vehicle-to-X communication connection. Furthermore, it is advantageous that the individual algorithms (regarding the weights) learned especially by means of several new scenarios on several motion and position sensors of several vehicles are transmitted to the cloud or the HIL and combined and fused there into an overall algorithm. For example, an average of the weights of the individual algorithms (individual KI (artificial intelligence)) can be performed. Additionally, the weights of the individual algorithms can be statistically combined advantageously. Furthermore, the weights of the individual algorithms can be weighted and / or dominated by the existing scenarios.

[0053] Furthermore, it can be proposed that (only) the vehicle position and the input variables are transmitted from the vehicle to a central and / or superior processing device and received by it. The reference position can be determined by the central and / or superior processing device itself here. The motion and position sensors preferably (only) send their currently calculated position and / or the currently main input variables to the central and / or superior processing device. Inside the processing unit, the highly accurate positioning of the corresponding vehicle is achieved, for example, by means of the vehicle-to-X communication connection (transmission time of the signal) for determining the reference position.

[0054] According to another advantageous design, it is proposed that steps i) to iv) are performed on the vehicle side. For example, steps i) to iv) can be performed by the motion and position sensors and / or the control device, especially by the control device for vehicle autonomous driving. Steps i) to iv) are especially performed on the vehicle side in the case of a test vehicle.

[0055] According to another advantageous design, it is proposed that the adjustment of the algorithm is achieved automatically. Preferably, the algorithm is a self-learning algorithm. Advantageously, measures based on or applying artificial intelligence (KI) methods are pursued. In other words, this especially means that the (learning) algorithm provides a calculation of the (protection limit) position inaccuracy of the (motion and position sensors) by using artificial intelligence (KI). This learning algorithm is, for example, artificial intelligence (KI) such as a neural network with weights. One or more of the above-mentioned input variables and / or the reference position can be used as input data in this neural network.

[0056] According to a favorable design, the algorithm is an (artificial) neural network with at least weights or thresholds. The neural network or KI uses the above input variables as inputs and calculates the statistical position accuracy of the (measured) vehicle position or the (GNSS) position calculated in the motion and position sensors with the help of the learning weights and / or thresholds within the network or KI.

[0057] According to a favorable design, at least one weight or threshold of the algorithm is adjusted in step d). The neural network or AI preferably uses the reference position (measured in step iii) for setting the (internal) weights and / or thresholds. The reference position can be collected by another positioning system. Especially in the case of a test vehicle, it is, for example, a (high-precision) GNSS reference system installed in the vehicle. Especially in the case of a production vehicle, in order to continuously adjust (improve) the network or KI, the alternative positions of the vehicle can be advantageously involved. Here, for example, it can be calculated from the transit time of the vehicle-to-X communication signal (e.g., relative to infrastructure points and / or adjacent vehicles) and / or by (high-precision) positioning of the vehicle with the help of features on the (digital) map and / or determined by means of environmental sensor data.

[0058] The (self-)learning of the network or KI for positioning accuracy (protection limit) is preferably achieved by comparing the reference position with the (actual) (GNSS-based) (vehicle) position calculated by the motion and position sensors or the GNSS-based combined (vehicle) position. The GNSS-based combined position is usually the GNSS position calculated by a Kalman filter, where not only satellite data, but also correction service data and / or wheel speed numbers, steering angles, accelerations, and / or rotation rates are used. The deviation between the GNSS (vehicle) position calculated by the motion and position sensors and the reference position can also be provided as an input to the network or KI. The algorithm, that is, the network or KI here, can thus learn the weights and / or thresholds for determining the position accuracy of the (protection limit in the motion and position sensors) based on the instantaneous position deviation of the (motion and position sensors) relative to the reference system and with the help of the above-mentioned (and additional) input variables. This can be achieved, for example, online in the motion and position sensors during a test drive or in a fleet already in the field, where the network or KI for position accuracy can always be further improved, especially as more scenarios are driven by vehicle types. Therefore, the neural network or KI (protection limit) for position accuracy can be advantageously learned with high accuracy, can cover as many scenarios as possible, and can also respond dynamically to new scenarios.

[0059] According to a favorable design, the adjustment of the algorithm is implemented when the vehicle is parked or afterwards. The adaptation of the algorithm, in particular the weights and / or thresholds of the neural network or KI for position accuracy (protection limit), can be achieved, for example, by storing the existing algorithm, in particular the existing network or the existing KI, in the shadow memory of the motion and position sensors. During vehicle operation, the algorithm, in particular the neural network or the KI, can be further adjusted (improved), particularly with respect to the weights and / or thresholds, only in this shadow memory. When the vehicle is parked or afterwards, (during operation) the newly learned part of the algorithm, in particular the newly learned neural network or the newly learned AI, can be used from the shadow memory as a replacement for the algorithm (network or KI) already used on the motion and position sensors for position accuracy (protection limit). Here, the memory content can be transferred regularly from the shadow memory to the normal motion and position sensor memory. This has the particular advantage that the algorithm (network or KI) can calculate the position accuracy (protection limit) in real time with the existing knowledge during operation, but can at the same time be further adjusted (improved) in the shadow memory with the help of new scenarios in the background.

[0060] According to a favorable design of the method for satellite-supported determination of the vehicle position, in order to adjust the algorithm, in particular the method proposed here for improving the accuracy estimation of satellite-supported determination of the vehicle position is carried out in step e).

[0061] According to another aspect, a computer program for performing the method given here is proposed. In other words, it particularly relates to a computer program (product) comprising instructions which, when executed by a computer, cause the computer to perform the method described herein.

[0062] According to another aspect, a machine-readable storage medium is proposed on which the computer program proposed here is stored. Usually, the computer-readable storage medium is a computer-readable data carrier.

[0063] According to another aspect, a motion and position sensor is proposed which is configured to perform the method proposed here. For example, the storage medium described above is a component of the motion and position sensor or is connected thereto. Advantageously, the motion and position sensor is arranged in or on a motor vehicle or is proposed or configured for installation in or on a motor vehicle. The motion and position sensor or the computing unit (processor) of the motion and position sensor can, for example, access the computer program described herein in order to perform the method described herein.

[0064] The motion and position sensor is preferably a GNSS sensor. The motion and position sensor can be a position and orientation sensor. Additionally, the GNSS sensor can be designed as a GNSS-based position and orientation sensor. The GNSS or the (vehicle's) motion and position sensor is necessary for automatic or autonomous driving and calculates a highly accurate vehicle position using satellite navigation data (GPS, GLONASS, Beidou, Galileo), which is also referred to as navigation satellite system data or GNSS data. This calculation is basically based on the transit time measurement of (electromagnetic) GNSS signals from at least four satellites. Additionally, correction data from a so-called correction service in the sensor can be used to calculate the vehicle's position more accurately. Together with the received GNSS data, a high-precision time (such as Coordinated Universal Time) is regularly read in the sensor and used for precise position determination. Other input data into the position sensor can be wheel speed, steering angle, and acceleration and rotational speed data. The motion and position sensor is preferably arranged to determine its own position, its own orientation, and its own speed based on GNSS data.

[0065] Accordingly, the details, features, and advantageous design options discussed in connection with the method for satellite-supported determination of a vehicle's position can also occur in the case of the method, motion and position sensor, computer program, and / or storage medium for improving the accuracy estimation of satellite-supported determination of a vehicle's position presented here, and vice versa. In this regard, full reference is made to the statements there to characterize the features in more detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The solution presented here and its technical environment are explained in more detail below with reference to the drawings. It should be noted that the present invention should not be limited by the illustrated embodiments. In particular, unless otherwise explicitly stated, partial aspects of the facts explained in the drawings can also be extracted and combined with other components and / or other drawings and / or the findings of this specification. The figures schematically show:

[0067] Figure 1 the flow of the method for satellite-supported determination of a vehicle's position presented here during normal operation, and

[0068] Figure 2 the flow of the method for improving the accuracy estimation of satellite-supported determination of a vehicle's position presented here during normal operation. DETAILED DESCRIPTION

[0069] Figure 1Schematically shows the flow of the method proposed herein for satellite-supported determination of vehicle position in normal operation. The sequence of processing steps a), b), c), d) and e) with blocks 110, 120, 130, 140 and 150 shown is merely exemplary. Receiving GNSS satellite data is implemented in block 110. In block 120, the determination of the vehicle position is implemented using the GNSS satellite data received in step a). In block 130, the provision of input variables is implemented, which may affect the accuracy of the vehicle position determined in step b). In block 140, the determination of the position accuracy of the vehicle position determined in step b) is implemented using an algorithm that assigns position accuracy to the vehicle position. Adjustment of the algorithm is implemented in block 150.

[0070] In particular, method steps a) and c) or b) and c) can also run at least partially in parallel or simultaneously.

[0071] Figure 2 Schematically shows the flow of the method proposed herein for improving the accuracy estimation of satellite-supported determination of vehicle position in normal operation. The shown sequence of method steps i), ii), iii) and iv) with blocks 210, 220, 230 and 240 is merely exemplary. Detection of the GNSS-based vehicle position is implemented in block 210. Receiving the input variables is implemented in block 220, which may affect the accuracy of the vehicle position detected in step i). In block 230, the detection of the reference position of the vehicle position detected in step i) is implemented. In block 240, the algorithm that assigns position accuracy to the vehicle position is adjusted considering at least one comparison between the vehicle position detected in step i) and the reference position detected in step iii) and / or at least one of the input variables received in step ii).

[0072] In particular, method steps i), ii) and iii) can also run at least partially in parallel or simultaneously.

[0073] In particular, the solutions presented herein can achieve one or more of the following advantages:

[0074] · By introducing a KI or neural network for learning the position accuracy determination function, memory on motion and position sensors can be saved.

[0075] · This new KI can calculate the position accuracy in motion and position sensors more accurately according to existing scenarios.

[0076] · The memory required in motion and position sensors is much smaller than the use of a look-up table.

[0077] · Using a reference positioning system in an autonomous vehicle to learn the KI (the deviation of the satellite-supported determined position from the reference position system) offers the possibility of continuously improving the KI for position accuracy on the motion and position sensors based on new scenarios of vehicles already present on site.

[0078] · By accurately calculating the position accuracy or outputting such improved position accuracy through motion and position sensors, the traffic safety of vehicles using motion and position sensors can be enhanced because other control devices exactly know whether they can trust the position of the motion and position sensors at this moment.

Claims

1. A method for establishing the positional accuracy of satellite-supported determination of a vehicle's position, comprising the following steps: Receiving GNSS satellite signals at at least one GNSS using a GNSS receiving unit, Determining the vehicle position of the vehicle using data from the received GNSS satellite signals, Determining the positional accuracy of the determined vehicle position by using an algorithm that assigns positional accuracy to the determined vehicle position, Providing the determined vehicle position and the assigned determined positional accuracy to a control device of the vehicle, and Adjusting the algorithm based on a comparison of the determined vehicle position with a reference position.

2. The method according to claim 1, wherein Determining the positional accuracy is performed by a vehicle component.

3. The method according to claim 1, wherein, Adjusting the algorithm is performed remotely from the vehicle, and the method further comprises: sending the adjusted algorithm to the vehicle.

4. The method according to any one of claims 1 to 3, wherein, The algorithm determines the positional accuracy based on at least one of the determined vehicle position and input variables that affect the accuracy of the determined measurement position.

5. A method for improving the accuracy estimation of satellite-supported determination of a vehicle's position, comprising the following steps: Detecting a GNSS-based vehicle position using a GNSS receiving unit, Receiving at least one input variable that may affect the accuracy of the detected GNSS-based vehicle position, and Detecting a reference position for the detected GNSS-based vehicle position, Adjusting an algorithm that assigns positional accuracy to the detected GNSS-based vehicle position by considering at least one of a comparison between the detected GNSS-based vehicle position and the detected reference position and a comparison between the detected GNSS-based vehicle position and at least one of the received input variables, and Assigning the assigned positional accuracy and at least one detected GNSS-based vehicle position and the detected reference position to a control device of the vehicle.

6. The method according to claim 5, wherein The method is performed at least partially remotely from the vehicle.

7. The method according to claim 5, wherein, The method is performed entirely by vehicle components.

8. The method according to any one of claims 5 to 7, wherein The adjustment of the algorithm is automatically achieved.

9. The method according to any one of claims 5 to 7, wherein The algorithm is a neural network having at least weights or thresholds.

10. The method according to claim 9, wherein, In adjusting the algorithm, at least one weight or threshold of the algorithm is adjusted.

11. The method according to any one of claims 5 to 7, wherein The adjustment of the algorithm is achieved when the vehicle is parked or afterwards.

12. The method according to any one of claims 1 to 4, wherein, To adjust the algorithm, the method according to any one of claims 5 to 11 is performed.

13. A machine-readable storage medium having a computer program stored thereon, the computer program for performing the method according to any one of claims 1 to 12.

14. A motion and position sensor configured to perform the method according to any one of claims 1 to 12.

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