4.6 Article

Human-Machine Collaboration for Automated Driving Using an Intelligent Two-Phase Haptic Interface

Journal

ADVANCED INTELLIGENT SYSTEMS
Volume 3, Issue 4, Pages -

Publisher

WILEY
DOI: 10.1002/aisy.202000229

Keywords

automated driving; human-machine collaborations; intelligent haptic interfaces; takeover controls

Funding

  1. SUG-NAP Grant of Nanyang Technological University [M4082268.050]
  2. A*STAR Grant of Singapore [1922500046]
  3. National Nature Science Foundation of China [51875302]
  4. State Key Laboratory of Automotive Safety and Energy [KF2021]

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This study proposes an intelligent haptic interface based on a two-phase human-machine interaction model, which can switch functionality between predictive guidance and haptic assistance based on the varying state and control ability of human drivers, helping drivers gradually resume manual control. The results from vehicle experiments suggest that this new method effectively enhances driving state recovery and control performance during takeover compared to existing approaches, improving the safety and smoothness of human-machine interaction in automated vehicles.
Prior to realizing fully autonomous driving, human intervention is periodically required to guarantee vehicle safety. This poses a new challenge in human-machine interaction, particularly during the control authority transition from automated functionality to a human driver. Herein, this challenge is addressed by proposing an intelligent haptic interface based on a newly developed two-phase human-machine interaction model. The intelligent haptic torque is applied to the steering wheel and switches its functionality between predictive guidance and haptic assistance according to the varying state and control ability of human drivers. This helps drivers gradually resume manual control during takeover. The developed approach is validated by conducting vehicle experiments with 26 participants. The results suggest that the proposed method effectively enhances the driving state recovery and control performance of human drivers during takeover compared with an existing approach. Thus, this new method further improves the safety and smoothness of human-machine interaction in automated vehicles.

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