Satellite Positioning Accuracy via Learning Algorithm
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Solution Overview
Problem
Current motion and position sensors for autonomous vehicles face challenges in accurately determining positional accuracy due to the limitations of lookup tables, which require vast amounts of memory and cannot cover all possible scenarios, leading to fluctuations in accuracy and the need for a more efficient method to calculate protection limits.
Innovation Solution
A learning algorithm is used to determine positional accuracy by assigning a statistical accuracy to a vehicle position based on input variables such as satellite constellation, signal strength, environmental sensors, and vehicle data, replacing the need for a lookup table and allowing for adaptive calculation of protection limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a lookup table is used to determine positional accuracy, then the determination can be made quickly, but the memory requirements become excessively large and cannot cover all possible scenarios
Solution Approach 1:
The patent transforms the lookup table approach into a parameter-based calculation method. Instead of storing pre-computed accuracy values for all possible scenarios in memory, the system uses input variables (satellite constellation, signal strength, environmental conditions, vehicle data) as parameters to dynamically calculate positional accuracy through algorithms. This changes the storage requirement from storing vast amounts of pre-computed data to storing only the calculation parameters and algorithms, significantly reducing memory usage while maintaining comprehensive scenario coverage.
2Ease of operation
If a lookup table is used to determine positional accuracy, then the calculation is simple, but it cannot cover all possible driving scenarios
Solution Approach 1:
The patent implements a dynamic calculation system where positional accuracy is determined through algorithms that process input variables representing current driving conditions (satellite constellation, signal strength, environmental sensors, vehicle data). Unlike a static lookup table that can only cover pre-defined scenarios, this dynamic system can adapt to any new scenario by processing the current state parameters through the calculation algorithm, ensuring comprehensive scenario coverage while maintaining computational efficiency.
3Measurement precision
If vast numbers of driving scenarios are recorded for lookup table parameterization, then the positional accuracy determination can be more accurate, but the data recording requirements become impractical
Solution Approach 1:
The patent replaces the mechanical/data-intensive approach of recording vast numbers of actual driving scenarios with a computational model that uses key input variables (satellite constellation, signal strength, environmental conditions, vehicle data) to calculate positional accuracy. Instead of physically recording and storing海量 driving scenario data, the system substitutes this with a streamlined parameter-based calculation approach that achieves the same statistical accuracy indication without the impractical data recording requirements.
Data Source
AI summary
The disclosure relates to a method for satellite-based determination of a vehicle position, comprising the following steps: a) receiving GNSS satellite data; b) determining a vehicle's position with the GNSS satellite data received in step a); c) providing input variables that can have an effect on the accuracy of the vehicle position determined in step b); d) determining a positional accuracy of the vehicle position determined in step b) using an algorithm that assigns a positional accuracy to a vehicle position; and e) adapting the algorithm.