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A workload adaptive haptic shared control scheme for semi-autonomous driving.

Ruikun Luo1, Yifan Weng2, Yifan Wang3

  • 1Robotics Institute, University of Michigan, Ann Arbor, MI, United States.

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This study introduces adaptive haptic shared control that adjusts to driver workload and attention. The new system improves driving performance and trust in autonomous vehicles.

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Area of Science:

  • Human-computer interaction
  • Autonomous systems
  • Automotive engineering

Background:

  • Haptic shared control allocates control between humans and autonomous agents in semi-autonomous driving.
  • Current systems often neglect the human operator's state, limiting effectiveness.

Purpose of the Study:

  • To develop and evaluate a novel haptic shared control scheme that dynamically adapts to human operator workload, visual attention (eyes on road), and steering input torque.
  • To investigate the impact of this adaptive scheme on human-machine interaction and driving performance.

Main Methods:

  • Human-in-the-loop experiments were conducted with 24 participants controlling a simulated High Mobility Multipurpose Wheeled Vehicle (HMMWV).
  • Participants shared control with an autonomous navigation module while performing a concurrent target detection task.
  • The control system was tested in both adaptive and non-adaptive configurations based on real-time human factors.

Main Results:

  • The adaptive haptic control scheme significantly reduced operator workload compared to the non-adaptive system.
  • Participants reported higher trust in the adaptive autonomy.
  • Driving task performance improved, and required control effort was reduced with the adaptive scheme.

Conclusions:

  • Adaptive haptic shared control that considers real-time human factors offers substantial benefits over non-adaptive approaches.
  • This adaptive strategy enhances safety, efficiency, and user acceptance in semi-autonomous driving systems.
  • Future research should explore further personalization and integration of adaptive shared control in diverse driving scenarios.