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Area of Science:

  • Intelligent Transportation Systems
  • Artificial Intelligence
  • Robotics

Background:

  • Connected and autonomous vehicles (CAVs) are vital for future transport, but mixed autonomy traffic (CAVs and human-driven vehicles) presents challenges.
  • Effective decision-making for CAVs is crucial for safety and efficiency during the transition to full autonomy.
  • Deep reinforcement learning (DRL) has shown promise, but graph reinforcement learning (GRL) offers superior capabilities for modeling complex vehicle interactions.

Purpose of the Study:

  • To provide a comprehensive review of GRL-based methods for CAV decision-making.
  • To establish a generic GRL framework for understanding autonomous driving decision technologies.
  • To identify challenges and future research directions in GRL for autonomous driving.

Main Methods:

  • Reviewing GRL methods from the perspective of mixed autonomy traffic construction.
  • Examining graph representation techniques for dynamic driving environments.
  • Summarizing related works on graph neural networks (GNN) and DRL in autonomous driving.
  • Compiling validation methods for assessing decision-making performance.

Main Results:

  • GRL methods demonstrate significant potential for improving CAV decision-making by accurately representing inter-vehicle effects and dynamic traffic.
  • The review categorizes GRL approaches based on traffic composition, environmental representation, and underlying network architectures.
  • Key validation strategies for GRL-based autonomous driving systems are identified.

Conclusions:

  • GRL is a promising approach for advancing CAV decision-making in mixed traffic environments.
  • Further research is needed to address the challenges and fully leverage the potential of GRL for autonomous driving.
  • This review serves as a foundational resource for researchers developing GRL-based solutions for autonomous vehicles.