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Drivers' Visual Behavior-Guided RRT Motion Planner for Autonomous On-Road Driving.

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Summary

This study introduces a novel motion planner for autonomous vehicles, guiding the rapidly exploring random tree (RRT) algorithm with drivers' visual behavior. This approach enhances trajectory naturalness and efficiency for real-world on-road driving.

Keywords:
RRT (rapidly-exploring random tree)autonomous vehicledrivers’ visual behaviormotion planningon-road driving

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Rapidly exploring random tree (RRT) algorithms are common for motion planning but often produce unnatural trajectories and are inefficient for autonomous vehicles.
  • Existing RRT methods struggle with practical on-road driving scenarios due to issues like useless sampling and slow exploration.

Purpose of the Study:

  • To develop a real-time motion planner for autonomous vehicles that overcomes the limitations of traditional RRT algorithms.
  • To integrate drivers' visual search behavior into the RRT framework for more natural and efficient motion planning.

Main Methods:

  • A novel RRT algorithm incorporating a guided sampling strategy based on drivers' visual search behavior.
  • Implementation of a continuous-curvature smoothing method using B-splines for trajectory refinement.
  • Real-world testing and verification on an actual autonomous vehicle across diverse traffic scenarios.

Main Results:

  • The proposed algorithm demonstrates feasibility and efficiency for on-road autonomous driving.
  • Experimental results confirm superior performance compared to previous motion planning algorithms.
  • Statistical analyses validate the effectiveness of the visual behavior-guided RRT approach.

Conclusions:

  • The visual behavior-guided RRT approach offers a significant advancement in autonomous vehicle motion planning.
  • This method enhances trajectory quality and planning efficiency for real-world driving applications.
  • The algorithm is a viable and effective solution for safe and efficient autonomous navigation.