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Modeling Human Steering Behavior in Haptic Shared Control of Autonomy-Enabled Unmanned Ground Vehicles.

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This study extends human steering models for autonomous vehicle control. The new model accurately predicts human performance in shared control systems, improving autonomous ground vehicle development.

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Autonomous Systems

Background:

  • Existing human steering models are limited to full teleoperation, not shared control with autonomy.
  • Understanding human interaction with autonomous systems in Unmanned Ground Vehicles (UGVs) is crucial for effective collaboration.

Purpose of the Study:

  • To extend human steering models for haptic shared control in autonomy-enabled UGVs.
  • To mathematically capture human operator responses to autonomy and model human-autonomy collaboration.

Main Methods:

  • Human subject tests were conducted to gather data for model development and validation.
  • The ACT-R architecture and a two-point steering model were adapted to predict operator steering angles.
  • A torque conversion module was developed to translate model commands into haptic feedback, and a parameterization strategy optimized performance based on Average Lane Keeping Error (ALKE).

Main Results:

  • The developed model successfully predicts the minimum Average Lane Keeping Error (ALKE) achieved by human subjects in a shared control setting.

Conclusions:

  • The extended human steering model accurately predicts optimal haptic shared control performance, measured by ALKE.
  • This model facilitates simulation-based engineering for developing and evaluating shared control technologies in UGVs.