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A Study on Dynamic Motion Planning for Autonomous Vehicles Based on Nonlinear Vehicle Model.

Xin Tang1, Boyuan Li2, Haiping Du3

  • 1Fok Ying Tung Research Institute, Hong Kong University of Science and Technology (HKUST), Guangzhou 511458, China.

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Summary

This study introduces a new motion planner for autonomous vehicles, utilizing a nonlinear model for improved obstacle avoidance. The approach ensures real-time performance and driving efficiency with reduced replanning.

Keywords:
Takagi–Sugeno fuzzy modelautonomous vehicleobstacle avoidancepath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Autonomous driving relies heavily on motion planning and trajectory tracking, areas requiring urgent improvement.
  • Current path planning methods often simplify vehicle dynamics using linear models to achieve real-time performance, sacrificing accuracy.
  • Accurate simulation of vehicle dynamics and kinematics alongside real-time computation is crucial for enhanced autonomous driving performance.

Purpose of the Study:

  • To develop an advanced motion planner that integrates nonlinear vehicle characteristics for superior performance.
  • To enhance dynamic obstacle avoidance capabilities in intelligent interconnected vehicles.
  • To ensure real-time path planning that aligns with the vehicle's dynamic behavior.

Main Methods:

  • Implementation of a Takagi-Sugeno fuzzy-model-based closed-loop rapidly exploring random tree (CL-RRT) algorithm for motion planning.
  • Direct integration of a nonlinear vehicle model into the motion planner design.
  • Application of Takagi-Sugeno fuzzy modeling for real-time utilization of vehicle state in path planning.

Main Results:

  • The proposed motion planner effectively improves dynamic obstacle avoidance.
  • Local obstacle avoidance paths are generated in accordance with the vehicle's dynamic characteristics.
  • Numerical simulations demonstrate the planner's ability to generate efficient reference trajectories with a lower replanning rate.

Conclusions:

  • The Takagi-Sugeno fuzzy-model-based CL-RRT approach successfully addresses the limitations of linear models in motion planning.
  • This method enables real-time path planning that accurately reflects vehicle dynamics, enhancing safety and efficiency.
  • The developed motion planner offers a significant advancement for autonomous driving systems, particularly in complex dynamic environments.