WO2025011969A1 - Method for determining protection levels during a journey by a vehicle with a gnss-supported localisation system with the aid of a copula-supported bayesian framework - Google Patents

Abstract

The invention relates to a method for determining protection levels (7) during a journey by a vehicle with a GNSS-supported localisation system using a copula-supported Bayesian framework, wherein a plurality of surroundings classes (10, 11) and a plurality of GNSS quality indicators (9) are predefined, and wherein for each surroundings class (10, 11) and each GNSS quality indicator (9) a copula model (15, 16, 17) is provided offline and stored in a memory, said method comprising the following steps: a) classifying the surroundings during the journey based on the GNSS quality indicators (9) and ascertaining which surroundings class (10, 11) the surroundings belong to, b) reading out, from the memory, a copula model (15, 16, 17) corresponding to the ascertained surroundings classes (10, 11) for the corresponding quality indicators (9), c) extracting at least one likelihood function (1, 2, 3, 4, 12) from the read-out copula model (15, 16, 17) according to a quality indicator value (13), d) ascertaining a posteriori distribution (6) for position errors in that the at least one likelihood function (1, 2, 3, 4, 12) is multiplied, based on Bayes' theorem, by a prior distribution (5) for errors, and e) determining protection levels (7) from the posteriori distribution (6).

Description

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State of the art

The present invention relates to a method for determining protection levels when driving a vehicle with a GNSS-supported localization system using a copula-supported Bayesian framework. Furthermore, a control unit, a computer program, a machine-readable storage medium and a localization system are specified. The invention can be used in particular in GNSS-supported localization systems for automated or autonomous driving.

It is known that positioning and navigation on earth and in the air can be carried out using a global navigation satellite system (GNSS) by receiving navigation satellite signals. With the help of multi-frequency and multi-constellation reception, positions on earth can be determined to within a centimeter. The quality of these positions results from the fact that the requirements placed on them, in particular accuracy, continuity, availability and integrity, are met.

It can be stated that in safety-critical autonomous driving, integrity such as positioning accuracy plays an extremely important role, since integrity ensures the reliability of positioning accuracy, and poor integrity monitoring can lead to catastrophic consequences in safety-critical environmental scenarios. The term “integrity” was originally introduced for positioning and navigation in the air and can be described in relation to position errors with the following parameters:

- AL (Alert Limit) describes a position error tolerance that must not be exceeded. Otherwise a warning message is triggered.

- TTA (Time to Alert) describes the maximum permissible time interval that can elapse between the AL being exceeded and the warning message being triggered.

- IR (Integrity Risk) describes the probability that the position error exceeds the AL.

- PE (Position Error) describes the deviation between the determined and the actual position.

- PL (Protection Level) describes the position error at which the algorithm guarantees that it will not be exceeded undetected.

- FA (False Alarm) describes the event in which a warning message is triggered without the AL being exceeded.

- Ml (Misleading Information) describes the event where the PL is smaller than the position error, and the PL and the position error are smaller than the AL.

- HMI (Hazardously Misleading Information) describes the event where the PL is less than the position error and the AL, and the position error exceeds the AL.

The Protection Level (PL) parameter is the core for integrity monitoring, which can be output together with the position from the localization system, and ensures that the entire system is safe if the position error is below the prescribed AL.

The known methods for determining protection levels were usually developed within the ABSA, GBAS or S BAS concept for positioning and navigation in the air (e.g. Greer et al., 2007, Gratton et al., 2010, Zhu et al., 2018), whose standardized integrity algorithms were usually defined taking into account the reception conditions of flying aircraft, and are therefore not suitable for positioning and navigation on earth for autonomous driving in view of many critical environmental conditions (e.g. in urban environments) and reception conditions (e.g. multipath reception). In addition, The procedures developed within the framework of the ABSA, GBAS or SBAS concepts are generally based on single-frequency reception. In contrast, autonomous driving, as mentioned at the beginning, requires multi-frequency and multi-constellation reception.

Although the well-known ARAIM concept (Blanch et al., 2007) was developed with multi-frequency and multi-constellation reception in mind to provide information on degraded GNSS satellites and is more robust than the ABSA, GBAS or SBAS concepts in terms of lower ionospheric propagation delay due to multi-frequency reception and higher measurement redundancy due to multi-constellation reception, it seems difficult to implement this concept in the process of determining protection levels for positioning and navigation on Earth. In this context, the following challenges exist in particular:

The use of non-ionospheric (IF) measurements is known to increase the magnitude of errors that are not correlated between frequencies, such as thermal noise, multipath effects or certain distortions. In a typical road environment, this can lead to large position uncertainties.

In addition, aviation GNSS receivers typically perform pseudorange phase smoothing over 100 seconds to reduce noise and multipath effects. This approach cannot be used in driving because carrier phase tracking is unlikely to be reliably maintained for very long due to environmental conditions.

In addition, for most planned applications in the automotive industry, a more stringent AL and TTA are expected than in aerospace (typically AL in the range of 0.5 to 10 meters and TTA in the range of 1 second), without necessarily requiring a less stringent integrity risk (IR). This means that the typical magnitude of the ARAIM concept may lead to an oversized PL.

It is therefore desirable to create a method for determining protection levels in the context of positioning and navigation on Earth, which is not only applicable for multi-frequency and Multi-constellation reception, but also for determining a robust protection level under critical environmental conditions. This is particularly important for autonomous driving, because autonomous driving places particularly high demands on the security and integrity or correctness of the location information in addition to position accuracy.

disclosure of the invention

A method for determining protection levels when driving a vehicle with a GNSS-supported localization system using a copula-supported Bayesian framework contributes to this, wherein a plurality of environmental classes and a plurality of GNSS quality indicators are predefined, and wherein for each environmental class and each GNSS quality indicator, a copula model is provided offline and stored in a memory, comprising the following steps: a) classifying the environment during the journey based on the GNSS quality indicators and determining which environmental class the environment belongs to, b) reading a copula model corresponding to the determined environmental class for the corresponding quality indicators from the memory, c) extracting at least one likelihood function from the read copula model according to a quality indicator value, d) determining a posterior distribution for errors by multiplying the at least one likelihood function based on Bayes' theorem with a prior distribution for position errors. and e) determining protection levels from the posterior distribution.

The method described is particularly suitable for autonomous driving. Autonomous driving here refers in particular to the movement of vehicles, mobile robots and driverless transport systems that behave largely autonomously using a GNSS receiver and based on global navigation satellite systems (GNSS). It is particularly advantageous if a self-driving vehicle is provided with a localization system for carrying out the method described. The localization system can be a GNSS-based system or a GNSS and INS-based system. Localization system that can detect GNSS satellites in its field of view, receive GNSS signals, and position based on received GNSS signals.

It can be provided that the described method is carried out epochally or regularly. An epoch here refers in particular to a time interval in which a currently determined position is valid or until a currently determined position is updated. This can mean that the described method is carried out once each time the position is updated, so that the currently determined protection level can be output together with the currently determined position. This can also mean that in an epoch all parameters relevant for position determination, such as Dilution of Precision (DOP) or the number of available GNSS signals in the field of view, are updated once.

Although the steps are indicated here with the letters a) to e) in a specific order, it is not necessary to always follow this order. For example, the individual steps can be repeated independently of one another and/or partially omitted in the case of repetition. It is possible that the steps are carried out at least partially at the same time.

The main idea of the invention is to provide copula models based on the training data acquired through test measurements and/or simulations offline in advance and to store them in a memory. A copula model describes the statistical relationship between data sets ordered by rank. A copula is a multivariate cumulative distribution function for which the marginal probability distribution of each variable is uniformly distributed over the interval [0, 1] (Schmidt, T. (2007). Coping with copulas. Copulas- Form theory to application in finance, 3-34). The copula models provided offline can be read from the memory during the online journey of a vehicle according to step b) and used to generate likelihood functions according to step c). Protection levels can thus be determined during the online journey according to steps d) and e) by the posterior distribution, which is determined by multiplying the likelihood functions with a prior distribution based on Bayes' theorem. In step d), the errors may be the deviations of all the GNSS-based localization system output quantities, e.g. position errors, speed errors and/or alignment errors. In step e), the protection levels can be determined based on a target integrity risk from the posterior distribution.

The invention has the advantage that the protection levels are implemented independently of variances.

When providing the copula models, it can be planned that the copula models are modelled with the training data taking into account different environmental conditions and GNSS quality indicators. It is possible to classify the environmental conditions into different environmental classes so that at least one copula model can be provided for each environmental class. It is also possible to predefine a number of GNSS quality indicators so that a copula model can be provided for each environmental class and for each GNSS quality indicator.

A GNSS quality indicator characterizes the quality of the position estimation situation for positioning algorithms and can be specified as a number between zero and a positive real number (see Fig. 2). Zero stands for very poor quality and the positive real number for very good quality. A GNSS quality indicator refers to key signals or key quantities of a GNSS that can be measured or calculated online.

A GNSS quality indicator can be Dilution of Precision (DOP). DOP is a quality measure for the available GNSS signals under line-of-sight conditions and describes how well the GNSS satellites that have emitted these available GNSS signals are suited for positioning at a location in their position relative to each other. A GNSS quality indicator can also be the number of available GNSS signals in the field of view. Since at least four GNSS signals are usually required for positioning, at least five GNSS signals for integrity monitoring and at least six GNSS signals for identifying defective GNSS satellites, the number of available GNSS signals is also extremely relevant for positioning quality.

As mentioned above, the determination of the position on earth is strongly influenced by the environmental conditions. It may be intended that the Environmental conditions are classified both in the offline modelling of the copula models and according to step a) in the online determination of protection levels.

It is possible that the environmental conditions are classified into different classes depending on the multipath effect caused by the obstacles in the GNSS signal propagation, namely an open environment class, an urban environment class and a critical environment class. The open environment class may include scenarios such as driving on open land, highway, etc., where there are almost no obstacles. The urban environment class may include scenarios such as driving in urban canyons where there are many buildings, thus preventing GNSS signal propagation and/or reflecting the GNSS signals, taking multipath reception into account. The critical environment may include scenarios such as driving on a multi-level road where several lanes are superimposed.

When driving online, both the DOP and the number of available GNSS signals are usually output together with the specific position. This means that the GNSS quality indicators can be measured and/or calculated online. It is also possible to determine (in advance) both the DOP and the number of available GNSS signals in a field of view according to location, time and movement patterns for the provision of the copula models. This is because each GNSS has a large number of GNSS satellites (e.g. Galileo with 28 satellites, GPS with 24 satellites) that are evenly distributed in the sky or in orbit and move according to a movement pattern. This means that only certain GNSS signals from certain GNSS satellites can be received in a certain location at a certain time interval. The movement patterns are accessible to the public and can be downloaded (e.g. from the International GNSS Service (ISG)).

It is preferred if a copula model is provided with the following steps: i) providing training data collected by test measurements and/or simulations, ii) classifying the training data into the environment classes, iii) providing an empirical copula for each environment class and quality indicator, and iv) fitting an analytical copula model to the empirical copula.

It is possible that the training data is collected from measurements in the real field and/or the simulated test data. The simulated test data can e.g.

This can be generated, for example, by a GNSS signal generator that is able to take multipath processing, i.e. the different environmental classes, into account.

It is preferred if in step c) the at least one likelihood function is extracted from the copula model with the following sub-steps:

1) Extracting a conditional distribution of position errors from the copula model, and

2) Obtaining at least one likelihood function from the conditional distribution according to the different GNSS quality indicators.

It is also preferred if, in sub-step 1), the conditional distribution is provided from the training data acquired by test measurements and/or simulations.

It is further preferred if in step d) the prior distribution is defined based on the training data and using a parametric distribution.

It is particularly preferred if in step d) the posterior distribution for position errors is determined for each epoch.

In contrast to the known methods for determining protection levels, which are based either on the estimated variance from a parameter estimation process or on the GNSS residuals from RAIM, the described method is carried out independently of the estimated variance, but on the GNSS quality indicators. The quality indicators are key signals or key variables of the system that are measured online and/or can be calculated and characterize the quality of the position estimation situation for the positioning algorithm.

For this purpose, Bayes' theorem is used to determine a posterior distribution for position errors at each epoch by multiplying a large number of likelihood functions with a prior. The likelihood functions are obtained from the conditional probability distribution of the position error (from the training data) for various GNSS quality indicators. One of the main advantages of the invention, especially in comparison to known methods, is the use of a copula to generate the likelihood functions. This makes it possible to obtain a reliable likelihood function and consequently to derive the posterior distribution directly. By directly deriving the posterior distribution, robust protection levels can be determined and thus the probability of an H Ml problem can be reduced.

It is preferred if a control device for the GNSS receiver is set up to carry out the described method.

It is also preferred if a computer program is used to carry out a method described here. In other words, this relates in particular to a computer program (product) comprising instructions which, when the program is executed by a computer, cause the computer to carry out a method described here.

It is also preferred if a machine-readable storage medium is used on which the computer program proposed here is stored. The machine-readable storage medium is usually a computer-readable data carrier.

It is particularly preferred if the localization system for a vehicle is set up to carry out a method described here.

The solution presented here and its technical environment are explained in more detail below using the figures. It should be noted that the invention is not intended to be limited by the embodiments shown. In particular, unless explicitly stated otherwise, it is also possible to extract partial aspects of the facts explained in the figures and to other components and/or findings from other figures and/or the present description. It shows schematically:

Fig. 1 : an exemplary function graph for likelihood functions, prior distribution and posterior distribution,

Fig. 2: Extracting a likelihood function from a copula model according to a quality indicator value,

Fig. 3: a block diagram for providing copula models, and

Fig. 4: a block diagram of a described method.

Fig. 1 and Fig. 2 show the mathematical functions used in the described method and the associated data, and are explained together here so that the invention can be better understood at the data level.

Fig. 1 shows a function graph in which the likelihood function P(Q \dx) 1 , the likelihood function P(Qiz\dx) 2 , the likelihood function P(Qis\dx) 3 , the likelihood function P(Qi4\dx) 4 , the prior distribution 5 and the posterior distribution 6 are compared with each other and distributed over the dx-axis 8 corresponding to the position error.

In order to obtain the protection levels 7 during the online journey, the respective likelihood functions 1, 2, 3, 4 are first multiplied by the prior distribution 5 based on Bayes' theorem, resulting in the posterior distribution 6, the two limits of which, as shown in Fig. 1, correspond to the protection levels 7 to be determined.

Each likelihood function 1, 2, 3, 4 is associated with a quality indicator (not shown). For example, in the online run, the extracting/extracted likelihood function 12, as shown in Fig. 2, can be extracted from the corresponding copula model (not shown) according to a quality indicator value 13. In Fig. 2 it can also be seen that a quality indicator 9 is scaled from zero to thirty-five. Fig. 3 shows a schematic of a process for providing copula models 15, 16, 17 presented here. The sequence of sub-steps i), ii), iii), and iv) with blocks 210, 220, 230, and 240 are merely examples. In block 210, training data is provided which is acquired through test measurements and/or simulations. In block 220, the provided training data is classified into the different environment classes 10, 11. In block 230, an empirical copula between quality indicators 9 and position errors is provided for each class 10, 11. In block 240, an analytical copula model is adapted to the empirical copula in order to provide the copula models 15, 16, 17.

Fig. 4 shows a schematic and exemplary block diagram of the described method for determining protection levels using a copula-based Bayesian framework, wherein a plurality of environment classes and a plurality of GNSS quality indicators are given, and wherein for each environment class and each GNSS quality indicator a copula model 15, 16, 17 is provided offline and stored in a memory (not shown). The method steps a), b), c), d) and e) are carried out epoch by epoch during the journey (i.e. online). The illustrated order of the method steps a), b), c), d) and e) with the block 110, the arrows 120, the blocks 130, 140 and 150 is merely an example. In block 110, the environment is classified during the journey based on the GNSS quality indicators 9 and it is determined which environment class the environment belongs to, whereby data 14 such as GNSS signal data and sensor data are recorded online. At arrows 120, a copula model 15, 16 or 17 corresponding to the determined environment class is read from the memory. In block 130, at least one likelihood function 1, 2, 3, 4, 12 is extracted from the read copula model 15, 16 or 17 according to a quality indicator value 13. In the two blocks 140, a posterior distribution 6 for position errors is determined, whereby first in the first block 140 the prior distribution 5 is defined based on the training data and using a parametric distribution, and then in the second block 140 the at least one likelihood function 1, 2, 3, 4, 12 is multiplied based on Bayes' theorem with a prior distribution 5 for position errors. In block 150, protection levels 7 are determined from the posterior distribution 6 based on a target integrity risk.

Claims

claims 1. Method for determining protection levels (7) when driving a vehicle with a GNSS-supported localization system using a copula-supported Bayesian framework, wherein a plurality of environment classes (10, 11) and a plurality of GNSS quality indicators (9) are predefined, and wherein for each environment class (10, 11) and each GNSS quality indicator (9) a copula model (15, 16, 17) is provided offline and stored in a memory, comprising the following steps: a) classifying the environment during the journey based on the GNSS quality indicators (9) and determining to which environment class (10, 11) the environment belongs, b) reading out a copula model (15, 16, 17) corresponding to the determined environment class (10, 11) for the corresponding quality indicators (9) from the memory, c) extracting at least one Likelihood function (1, 2, 3, 4, 12) from the read copula model (15, 16, 17) according to a quality indicator value (13), d) determining a posterior distribution (6) for errors by multiplying the at least one likelihood function (1, 2, 3, 4, 12) based on Bayes' theorem with a prior distribution (5) for position errors, and e) determining protection levels (7) from the posterior distribution (6) 2. The method according to claim 1, wherein a copula model (15, 16, 17) is provided with the following steps: i) providing training data acquired by test measurements and/or simulations, ii) classifying the training data into the environment classes (10, 11), iii) providing an empirical copula for each environment class (10, 11) and each quality indicator (9), and iv) Fitting an analytical copula model (15, 16, 17) to the empirical copula. 3. Method according to claim 1 or 2, wherein in step c) the at least one likelihood function is extracted from the copula model (15, 16, 17) with the following substeps: 1) Extracting a conditional distribution of position errors from the copula model (15, 16, 17), and 2) Obtaining at least one likelihood function (1, 2, 3, 4) from the conditional distribution according to the different GN SS quality indicators (9). 4. The method according to claim 3, wherein in substep 1) the conditional distribution is provided from the training data acquired by test measurements and/or simulations. 5. Method according to one of the preceding claims, wherein in step d) the prior distribution (5) is defined based on the training data and using a parametric distribution. 6. Method according to one of the preceding claims, wherein in step d) the posterior distribution (6) for errors is determined for each epoch. 7. Method according to one of the preceding claims, wherein the errors affect all quantities output by the GNSS-based localization system. 8. Method according to one of the preceding claims, wherein the errors are position errors, speed errors and/or alignment errors. 9. Control device which is configured to carry out a method according to one of the preceding claims. 10. Computer program for carrying out a method according to one of the preceding claims 1 to 8. 11. Machine-readable storage medium on which the computer program according to claim 10 is stored 12. Localization system for a vehicle, set up to carry out a method according to one of claims 1 to 8.