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An Optimized Trajectory Planner and Motion Controller Framework for Autonomous Driving in Unstructured Environments.

Lu Xiong1,2, Zhiqiang Fu1,2, Dequan Zeng1,2

  • 1School of Automotive Studies, Tongji University, Shanghai 201804, China.

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|July 2, 2021
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Summary
This summary is machine-generated.

This study presents an optimized trajectory and motion planner for autonomous vehicles, enhancing obstacle avoidance in unstructured environments. The framework ensures safe and smooth navigation along reference roads.

Keywords:
autonomous drivingmodel predictive controlmotion controllerobstacle avoidancetrajectory planner

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Autonomous driving in unstructured environments presents significant challenges for navigation and obstacle avoidance.
  • Existing trajectory and motion planning methods often struggle with complex road geometries and dynamic obstacles.

Purpose of the Study:

  • To propose an optimized trajectory and motion planner framework for autonomous driving.
  • To enable safe and smooth obstacle avoidance along a reference road in unstructured environments.

Main Methods:

  • Decomposition of trajectory planning into lateral and longitudinal sub-tasks.
  • Utilizing a vehicle kinematic model for lateral movement and road smoothing via nonlinear optimization.
  • Employing a multilayered search algorithm for path boundary determination and an optimized path planner considering road distance and curvature.
  • Implementing an optimized speed planner with space-domain speed boundaries and acceleration constraints, solved using numerical optimization.
  • Developing a motion controller based on a kinematic error model for trajectory tracking.

Main Results:

  • The proposed framework effectively handles obstacle avoidance along reference roads in unstructured environments.
  • Experimental results demonstrate safe and smooth navigation capabilities.
  • The trajectory planner successfully smooths the reference road and finds optimal paths.

Conclusions:

  • The developed trajectory and motion planner framework is effective for autonomous driving in challenging, unstructured environments.
  • The system ensures safe and smooth obstacle avoidance, adhering to road constraints.
  • The integrated approach of path and speed planning, coupled with a robust motion controller, proves successful.