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Related Concept Videos

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Related Experiment Video

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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Neural Network Based Uncertainty Prediction for Autonomous Vehicle Application.

Feihu Zhang1, Clara Marina Martinez2, Daniel Clarke3

  • 1School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China.

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|May 29, 2019
PubMed
Summary

This study introduces a novel framework for predicting uncertainty in autonomous vehicle localization using artificial neural networks. The method effectively fuses sensor data and corrects odometry errors, enhancing navigation accuracy.

Keywords:
autonomous drivinglocalizationneural networkodometryuncertainty prediction

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Sensor Fusion

Background:

  • Complex fusion networks often face challenges with sporadically available signals.
  • Sensor characteristics are frequently unavailable, necessitating data-driven uncertainty modeling.
  • Accurate uncertainty estimation is crucial for reliable autonomous vehicle operation.

Purpose of the Study:

  • To propose a framework for uncertainty prediction in complex fusion networks with sporadic data.
  • To develop a method for modeling sensor uncertainty directly from data using artificial neural networks.
  • To apply and validate the framework for autonomous vehicle localization.

Main Methods:

  • A data-driven surrogated model of sensor uncertainty was developed using artificial neural networks.
  • The framework was applied to autonomous vehicle localization using odometry sensors (speed and orientation).
  • The methodology estimates location uncertainty within the vehicle's trajectory.

Main Results:

  • The proposed strategy enables effective fusion of autonomous vehicle location measurements.
  • The method successfully corrects accumulated odometry errors in various scenarios.
  • The applicability and generalization capacity of neural networks for uncertainty estimation were demonstrated.

Conclusions:

  • The presented methodology is suitable for uncertainty estimation in non-linear and intractable processes.
  • The framework offers a robust solution for enhancing autonomous vehicle localization accuracy.
  • Data-driven neural network models provide a powerful tool for sensor uncertainty prediction.