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Learning-Based Hierarchical Decision-Making Framework for Automatic Driving in Incompletely Connected Traffic

Fan Yang1, Xueyuan Li1, Qi Liu1

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|April 27, 2024
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This study introduces a stable hierarchical decision-making framework for autonomous driving using image input. It effectively handles real-world driving scenarios without needing global network information.

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Area of Science:

  • Robotics
  • Computer Science
  • Artificial Intelligence

Background:

  • End-to-end decision-making algorithms offer strong data processing but suffer from uncertainty.
  • Existing learning-based methods require ideal state information, limiting their real-world autonomous driving applicability.
  • Autonomous driving in complex environments necessitates robust decision-making with incomplete information.

Purpose of the Study:

  • To propose a stable hierarchical decision-making framework for autonomous driving systems.
  • To address the limitations of existing algorithms in handling real-world, incomplete information.
  • To enable reliable autonomous driving using only image input.

Main Methods:

  • A model-based data encoder transforms input images into a universal data format.
  • A state machine utilizing a time series Graph Convolutional Network (GCN) classifies driving states.
  • Rule-based algorithms are selected for action generation based on classified driving states.

Main Results:

  • The proposed framework successfully performs autonomous driving tasks across diverse traffic scenarios.
  • The system operates effectively without requiring global network information.
  • Comparative experiments validate the efficacy of the hierarchical framework, image data encoder, and time series GCN.

Conclusions:

  • The hierarchical decision-making framework provides a stable and effective solution for autonomous driving.
  • The model-based image encoder and time series GCN are crucial components for robust state classification and decision-making.
  • This approach enhances the reliability of autonomous vehicles in real-world conditions.