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Computational Efficient Motion Planning Method for Automated Vehicles Considering Dynamic Obstacle Avoidance and

Yuxiang Zhang1, Jiachen Wang2, Jidong Lv3

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Summary
This summary is machine-generated.

This study introduces an efficient motion planning method for automated vehicles, improving decision-making and trajectory generation in complex traffic. The approach enhances adaptive responses and computational efficiency for safer autonomous driving.

Keywords:
autonomous vehiclesmodel predictive controltrajectory planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Automotive Engineering

Background:

  • Complex driving scenarios demand adaptive and computationally efficient motion planning for automated vehicles.
  • Current methods often struggle with balancing decision-making complexity and real-time performance.

Purpose of the Study:

  • To propose a computationally efficient motion planning method for automated vehicles.
  • To enhance adaptive response and traffic interaction capabilities.
  • To accelerate trajectory generation for real-time application.

Main Methods:

  • Behavior decision-making simplified by connecting points on unequally divided road segments and lane centerlines.
  • Trajectory generation incorporates a dynamic vehicle model with changeable longitudinal velocity.
  • The C/GMRES algorithm accelerates on-line solving in nonlinear model predictive control.
  • Traffic participant motion prediction enhanced by driver intention and kinematic models.

Main Results:

  • The proposed method simplifies decision-making in both space and time.
  • Accelerated trajectory generation enables on-line solving.
  • Improved prediction of other traffic participants leads to more reasonable host vehicle behavior and trajectories.
  • Simulation results confirm the method's effectiveness.

Conclusions:

  • The developed motion planning method significantly improves computational efficiency for automated vehicles.
  • It enables more reasonable and adaptive behavior in complex traffic interactions.
  • The approach is effective for real-time autonomous driving applications.