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Interactive Cognition of Self-driving: A Multidimensional Analysis Model and Implementation.

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This study introduces self-driving interactive cognition, enhancing vehicle understanding of human intentions in complex environments. A novel method evaluates algorithms for trustworthy, human-friendly autonomous systems.

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Human-Computer Interaction

Background:

  • Self-driving vehicles face challenges in interactive analysis due to diverse data and complex environments.
  • Current systems struggle with understanding vehicle behavior and achieving human-AI synergy.
  • Lack of clear interactive cognition hinders trust and seamless integration of autonomous vehicles.

Purpose of the Study:

  • To highlight the significance of self-driving interactive cognition for enhancing autonomous system intelligence.
  • To establish a framework for analyzing and evaluating machine interactive cognition in complex scenarios.
  • To develop methods for achieving better human-vehicle interaction and trust.

Main Methods:

  • Introduced a multidimensional analysis model for self-driving interactive cognition.
  • Developed a multiview spatiotemporal graph convolutional network (MV-STGCN) for action recognition.
  • Proposed a nonlinear-CRITIC-TOPSIS method for efficient evaluation of interactive cognition algorithms.

Main Results:

  • Demonstrated the feasibility of interactive cognition inspired by the Wiener model in complex environments.
  • MV-STGCN model successfully enabled vehicle-to-human body language interactive cognition.
  • The nonlinear-CRITIC-TOPSIS method efficiently analyzed and compared different action recognition algorithms.

Conclusions:

  • Self-driving interactive cognition is crucial for developing intelligent and trustworthy autonomous systems.
  • Future vehicles will exhibit multichannel, cross-modal perception and human-friendly interaction.
  • "Self-driving + interactive cognition" paves the way for trusted, human-serving robotic vehicles.