Bayesian GNSS Protection Levels for Multi-Frequency Vehicle Location
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Solution Overview
Problem
Existing methods for determining protection levels in GNSS-based locating systems for autonomous driving are not suitable for multi-frequency and multi-constellation reception, leading to large position uncertainties and oversized protection levels due to environmental conditions and stringent accuracy demands.
Innovation Solution
A method using a Bayes' framework to determine protection levels by providing a first probability distribution based on training data, determining GNSS quality indicators epoch by epoch, and applying Bayes' theorem to calculate protection levels using multivariate, bivariate, or univariate conditional distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If known integrity algorithms from aviation (ABSA, GBAS, SBAS) are used for autonomous driving, then single-frequency reception is sufficient, but multi-frequency and multi-constellation reception cannot be properly utilized leading to large position uncertainties
Solution Approach 1:
The patent transforms the protection level determination from a single-value calculation to a probabilistic distribution approach. By modeling the protection level as a random variable with probability distribution functions that incorporate multiple frequency and constellation parameters, the system can properly utilize multi-frequency and multi-constellation reception data to reduce position uncertainty while maintaining adaptability to different GNSS configurations.
2Reliability
If ARAIM concept with multi-frequency and multi-constellation reception is implemented, then ionospheric delay and measurement redundancy improve, but computational complexity and difficulty of implementation increase significantly
Solution Approach 1:
The patent replaces the complex mechanical/computational system of traditional ARAIM protection level calculations with a probabilistic modeling approach. Instead of directly computing protection levels through complex iterative algorithms, the system uses probability distribution functions and statistical methods to model the uncertainty and determine protection levels, significantly reducing computational complexity while maintaining or improving reliability.
Solution Approach 2:
The patent changes the fundamental parameter representation from deterministic values to probabilistic distributions. By expressing protection levels, position errors, and measurement uncertainties as random variables with associated probability distributions, the system simplifies the computational framework while incorporating the benefits of multi-frequency and multi-constellation reception for improved reliability.
3Measurement precision
If stringent alert limits and time to alert requirements for autonomous driving are applied, then positioning accuracy demands increase, but protection levels become oversized leading to excessive false alarms
Solution Approach 1:
The patent applies partial action by using probabilistic thresholds rather than fixed deterministic thresholds for protection level determination. Instead of applying a single stringent alert limit that causes excessive false alarms, the system uses probability distribution functions to determine dynamic protection levels that adapt to current signal conditions, achieving the required positioning accuracy while maintaining appropriate false alarm rates.
Solution Approach 2:
The patent introduces dynamics into the protection level determination by making protection levels time-varying and condition-dependent. Instead of static alert limits, the system continuously updates protection levels based on current GNSS signal quality, geometric dilution of precision, and other dynamic parameters, allowing the system to meet stringent accuracy demands without causing excessive false alarms in varying environmental conditions.
Data Source
AI summary
A method for determining protection levels of a GNSS-based locating system for a vehicle is disclosed. The method includes providing at least one first probability distribution for a safety-relevant error as a function of GNSS quality indicators with the aid of training data such that the GNSS quality indicators were predetermined as random variables of the at least one first probability distribution based on the training data, the values of which can be determined epoch by epoch while the vehicle is traveling, wherein the at least one first probability distribution was stored in advance and can be used to determine protection levels while the vehicle is traveling. The method further includes determining protection levels while the vehicle is traveling with the following sub-steps (i) determining the values of the respective GNSS quality indicators for the current epoch, (ii) determining a posteriori distribution from the at least one first probability distribution with the determined values of the respective GNSS quality indicators based on Bayes' theorem, (iii) determining a protection level from the posteriori distribution for the current epoch, and (iv) repeating the sub-steps (i) to (iii) for determining a protection level for the next epoch.