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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving.

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  • 1School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.

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This study introduces a graph neural network reinforcement learning algorithm (SGRL) for autonomous driving decision-making. The SGRL algorithm enhances agent interaction and decision-making efficiency in complex environments.

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Area of Science:

  • Autonomous Driving Systems
  • Artificial Intelligence
  • Machine Learning

Background:

  • Autonomous driving decision-making relies on sensing systems and increasingly complex environments necessitate advanced algorithms.
  • Learning-based algorithms offer advantages in processing and understanding driving data.
  • Incorporating interactive information between agents is crucial for robust decision-making.

Purpose of the Study:

  • To propose a generalized single-vehicle-based graph neural network reinforcement learning algorithm (SGRL) for autonomous driving.
  • To enhance the decision-making process by incorporating interactive information between agents.
  • To improve network convergence, decision-making effectiveness, and training efficiency.

Main Methods:

  • Introduced graph convolution into the traditional Deep Q-Network (DQN) algorithm, creating the SGRL algorithm.
  • Employed a single-agent training methodology.
  • Designed an explicit incentive reward function and expanded the action space dimension.

Main Results:

  • The SGRL algorithm demonstrated superior network convergence compared to traditional DQN (NGRL) and multi-agent training (MGRL) algorithms.
  • Significant improvements were observed in the decision-making effectiveness of the SGRL algorithm.
  • The SGRL algorithm exhibited enhanced training efficiency in the highway ramp scenario.

Conclusions:

  • The proposed SGRL algorithm effectively incorporates interactive agent information into autonomous driving decision-making.
  • SGRL offers a promising approach for improving the performance and efficiency of learning-based autonomous driving systems.
  • The method shows significant advantages over existing algorithms in complex driving scenarios.