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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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Nonlinear Constrained Moving Horizon Estimation Applied to Vehicle Position Estimation.

Jonathan Brembeck1

  • 1Institute of System Dynamics and Control, Robotics and Mechatronics Center, German Aerospace Center (DLR), 82234 Weßling, Germany. jonathan.brembeck@dlr.de.

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Summary
This summary is machine-generated.

This study presents an advanced vehicle state observer for autonomous vehicles, improving safety and path following. The novel approach enhances estimation accuracy using real-world data.

Keywords:
GNSSIMUINSKalman filterautomotive applicationsconstrained estimationmoving horizon estimationnonlinear gradient descent searchnonlinear observervehicle state estimation

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Automotive Engineering

Background:

  • Designing high-performance state estimators for autonomous vehicles is complex due to increasing demands for safety and operational capabilities.
  • Accurate estimation of vehicle state (position, yaw angle, velocity, yaw rate) is crucial for path following control in autonomous driving.

Purpose of the Study:

  • To develop and evaluate a robust vehicle state observer for autonomous vehicles capable of handling complex scenarios and improving estimation accuracy.
  • To address challenges in state estimation, including vehicle standstill situations and integration of delayed/out-of-sequence Global Navigation Satellite System (GNSS) data.

Main Methods:

  • An extended moving horizon state estimation algorithm was employed, incorporating map-based road boundary information and an automatic event handling system.
  • The observer model design balanced complexity and performance, utilizing an efficient root search algorithm for map data extraction.
  • The proposed method was validated against a conventional extended Kalman filter using real-world vehicle test drive data.

Main Results:

  • The proposed moving horizon observer demonstrated highly promising results in real-world experiments, outperforming the conventional extended Kalman filter in accuracy and robustness.
  • The framework successfully integrated delayed and out-of-sequence Global Navigation Satellite System (GNSS) measurements.
  • The observer effectively utilized road boundary information to constrain vehicle position estimation, enhancing reliability.

Conclusions:

  • The developed vehicle state observer offers a significant advancement for autonomous vehicle navigation, providing accurate and reliable state estimation.
  • The approach effectively handles challenging conditions such as vehicle standstill and integrates diverse sensor data, paving the way for safer autonomous driving.
  • The moving horizon estimation technique shows strong potential for future autonomous vehicle applications, outperforming traditional methods.