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Area of Science:

  • Robotics
  • Automotive Engineering
  • Control Systems

Background:

  • Traditional automatic parking systems require excessive space due to limited sensing and positioning accuracy.
  • Future autonomous vehicles will feature enhanced perception and positioning, enabling more efficient parking maneuvers.
  • Optimizing parking space and trial count is crucial for autonomous driving, especially with unoccupied vehicles.

Purpose of the Study:

  • To derive closed-form solutions for the minimum internal and external space required for automatic parallel parking.
  • To determine the minimum number of trials needed for parallel parking using circular arcs.
  • To provide solutions for minimum required space for single-trial parking, considering both circular arcs and continuous-curvature curves.

Main Methods:

  • Development of closed-form mathematical solutions for space and trial calculations.
  • Analysis of parking maneuvers based on circular arc trajectories.
  • Extension of analysis to continuous-curvature curves for single-trial parking scenarios.

Main Results:

  • Closed-form solutions for minimum space and trials in automatic parallel parking (circular arcs).
  • Solutions for minimum space requirements for single-trial parking using circular arcs and continuous-curvature curves.
  • Systematic analysis addressing a gap in current research on automated parking space optimization.

Conclusions:

  • The study offers precise mathematical tools for optimizing automated parallel parking.
  • These findings are vital for developing efficient and space-conscious autonomous parking systems.
  • The research provides a foundational analysis for future advancements in autonomous vehicle maneuvering.