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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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A Trajectory Planning Method for Autonomous Valet Parking via Solving an Optimal Control Problem.

Chen Chen1, Bing Wu1, Liang Xuan1

  • 1College of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China.

Sensors (Basel, Switzerland)
|November 14, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an optimal control method for autonomous valet parking (AVP) trajectory planning. The approach ensures efficient and real-time parking maneuvers, improving practical applications.

Keywords:
autonomous parkinghomotopic methodoptimal controltrajectory planning

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Area of Science:

  • Robotics and Control Systems
  • Autonomous Driving Technology
  • Computational Optimization

Background:

  • Traditional parking planning research has prioritized path planning over trajectory planning.
  • Trajectory planning offers more practical insights for autonomous valet parking (AVP).
  • Existing methods may lack efficiency or real-time capabilities for dynamic parking scenarios.

Purpose of the Study:

  • To propose an optimal control-based trajectory planning method for autonomous valet parking (AVP).
  • To develop a method that considers dynamic obstacle avoidance and terminal constraints.
  • To achieve efficient and real-time trajectory planning for practical AVP systems.

Main Methods:

  • Vehicle kinematics model incorporating dynamic obstacle avoidance and terminal constraints.
  • Optimal control problem formulation with parking time and distance as cost functions.
  • Gauss pseudospectral method (GPM) for discretization and sequential quadratic programming for solving the nonlinear programming (NLP) problem.
  • Online calculation using a pre-computed offline trajectory as an initial guess.

Main Results:

  • The proposed method effectively solves the AVP trajectory planning problem.
  • The online calculation time for vertical parking trajectories is under 2 seconds, meeting real-time requirements.
  • Simulation results in a small parking lot scenario validate the algorithm's efficiency.

Conclusions:

  • The developed trajectory planning method is efficient and suitable for real-time autonomous valet parking.
  • The integration of GPM and an online initial guess strategy enhances parking maneuver performance.
  • This research contributes to the advancement of practical autonomous parking solutions.