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An Obstacle Avoidance Path Planning and Evaluation Method for Intelligent Vehicles Based on the B-Spline Algorithm.

Yulong Zhang1, Pengwei Wang1, Kaichen Cui1

  • 1School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255022, China.

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Summary
This summary is machine-generated.

This study introduces a new method for intelligent vehicles to plan obstacle avoidance paths in real-time. The approach ensures safe and smooth navigation in dynamic traffic, enhancing vehicle maneuverability.

Keywords:
B-splinecomprehensive evaluationfour-segment lane-changing modelintelligent vehiclepath planning

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Area of Science:

  • Robotics
  • Intelligent Transportation Systems
  • Control Engineering

Background:

  • Intelligent vehicles require real-time path planning for dynamic traffic scenarios.
  • Existing methods may not adequately address safety, smoothness, and comfort simultaneously.

Purpose of the Study:

  • To propose a novel path planning and evaluation method for intelligent vehicles.
  • To ensure real-time obstacle avoidance in dynamic traffic.
  • To optimize paths based on safety, smoothness, and comfort.

Main Methods:

  • Developed an obstacle avoidance path planning framework using the B-spline algorithm and four-stage lane-changing theory.
  • Established a comprehensive real-time path evaluation mechanism considering safety, smoothness, and comfort.
  • Validated the approach through co-simulation and real vehicle testing.

Main Results:

  • The proposed algorithm successfully generated real-time, available obstacle avoidance paths.
  • Simulated dynamic obstacle avoidance resulted in controlled lateral acceleration (±2.39 m/s²), yaw angle (±13.31°), yaw rate (±13.26°/s), and roll angle (±0.938°).
  • The evaluation mechanism effectively identified optimal paths for the given scenarios.

Conclusions:

  • The developed method meets the real-time path planning requirements for intelligent vehicles.
  • The integrated evaluation mechanism enhances path selection for optimal performance.
  • The approach is validated for practical application in dynamic traffic environments.