Particle Filter GNSS Distance Assessment for Satellite Tolling
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Solution Overview
Problem
Satellite-based tolling systems face inaccuracies in detecting vehicle passages into and out of zones, crossing virtual gantries, and calculating traveled distances due to erroneous position estimates from GNSS, leading to reduced user confidence and increased operational costs.
Innovation Solution
A method utilizing a Particle filter system that provides a probability distribution of vehicle positions, allowing for improved identification of low-confidence situations and accurate assessment of distance traveled by combining GNSS data with additional sensors and error models, such as Student's t-distribution for measurement noise, to enhance the reliability of satellite-based tolling systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional GNSS positioning is used for tolling systems, then the system is simple to operate, but the position estimation accuracy deteriorates in areas with sky obstructions
Solution Approach 1:
The patent introduces a particle filter algorithm as an intermediary processing layer between raw GNSS measurements and tolling decisions. This filter combines multiple measurement models (including Student's t-distribution for measurement noise) to produce more reliable position estimates without requiring hardware modifications, thus improving accuracy while maintaining operational simplicity
Solution Approach 2:
The patent changes the statistical parameters used in position estimation by employing Student's t-distribution instead of traditional Gaussian assumptions for measurement noise. This parameter change allows the system to better handle outliers and sky obstruction effects, improving position accuracy in challenging environments
2Measurement precision
If particle filter with multiple measurement models is used, then position estimation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent implements a practical particle filter that uses a finite number of particles (e.g., 100-1000 particles) rather than infinite precision, and selectively applies different measurement models based on signal conditions. This partial action approach achieves sufficient accuracy for tolling applications while keeping computational requirements within the capabilities of standard OBUs
Solution Approach 2:
The particle filter algorithm is designed to be self-adaptive, automatically selecting appropriate measurement models based on the current signal environment without requiring external intervention or complex configuration. The system self-regulates computational resource usage based on the number of visible satellites and signal quality metrics
3Reliability
If GNSS position estimates are used for tolling decisions, then the system is easy to operate, but false assessments and overcharging occur due to erroneous positions
Solution Approach 1:
The patent implements a feedback mechanism where particle filter position estimates are continuously monitored and compared against expected trajectory patterns. When deviations exceed thresholds (indicating potential false assessments), the system triggers verification procedures or adjusts tolling decisions, thereby improving reliability while maintaining ease of operation through automated anomaly detection
Solution Approach 2:
The system applies beforehand cushioning by using particle filter predictions to anticipate valid position ranges before making tolling decisions. By pre-establishing acceptable position deviation thresholds and using multiple measurement models to cushion against outliers, the system prevents false assessments before they occur, maintaining both reliability and operational simplicity
Data Source
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AI summary
Method for assessing distance driven by a vehicle utilizing a GNSS system comprising an OBU in the vehicles. The method comprises the steps of A) obtaining an initial vehicle position including some degree of uncertainty by any applicable method, B) assigning for each vehicle, using the Sequential Monte Carlo Method, a measurement model and a probability distribution a pre-determined number of particles, and assigning to each particle: i) a common initial probability, ii) an initial state comprising at least three dimensional spatial position, and C) defining epochs in time within each of which the following substeps: i) a prediction step (52), using said process model to predict with uncertainty the state of each particle in the next epoch, ii) an updating step (53), updating the particles' probability according to how well each particle's state from the prediction step agrees with GNSS pseudo range measurements, iv) recursively repeating steps i) to iii) above at any desired rate.