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EP-4740042-A1 - 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

EP4740042A1EP 4740042 A1EP4740042 A1EP 4740042A1EP-4740042-A1

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).

Inventors

  • Sgarz, Elena
  • STROBEL, JENS
  • METZGER, ALEXANDER
  • TOURIAN, Mohammad

Assignees

  • Robert Bosch GmbH

Dates

Publication Date
20260513
Application Date
20240627

Claims (12)

  1. 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. 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. 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. 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. 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. 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. 7. Method according to one of the preceding claims, wherein the errors affect all quantities output by the GNSS-based localization system.
  8. 8. Method according to one of the preceding claims, wherein the errors are position errors, speed errors and/or alignment errors.
  9. 9. Control device which is configured to carry out a method according to one of the preceding claims.
  10. 10. Computer program for carrying out a method according to one of the preceding claims 1 to 8.
  11. 11. Machine-readable storage medium on which the computer program according to claim 10 is stored
  12. 12. Localization system for a vehicle, set up to carry out a method according to one of claims 1 to 8.

Description

Description title Procedure for of protection levels when driving a with a GNSS with the help of a ten Bavesian framework 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