Modified Hybrid A* Collision-Free Path-Planning for Automated Reverse Parking

2025-12-12

AI Summary

In confined spaces, automatic parallel parking often fails due to the scarcity of feasible, collision-free trajectories. Method: This paper proposes an enhanced Hybrid A* algorithm that intrinsically embeds the vehicle’s unicycle kinematic model into the search process to generate kinematically feasible motion primitives directly. It further integrates centerline reconstruction and binary occupancy map inflation to achieve high-precision static obstacle avoidance. Contribution/Results: The method guarantees both global collision-free execution and kinematic feasibility while significantly improving trajectory smoothness and real-time performance. Simulation results demonstrate a 100% parking success rate and superior planning efficiency compared to baseline approaches. The proposed solution thus provides a robust and computationally efficient path planning framework for autonomous parking in tight environments.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Parking a vehicle in tight spaces is a challenging task to perform due to the scarcity of feasible paths that are also collision-free. This paper presents a strategy to tackle this kind of maneuver with a modified Hybrid-A* path-planning algorithm that combines the feasibility guarantee inherent in the standard Hybrid A* algorithm with the addition of static obstacle collision avoidance. A kinematic single-track model is derived to describe the low-speed motion of the vehicle, which is subsequently used as the motion model in the Hybrid A* path-planning algorithm to generate feasible motion primitive branches. The model states are also used to reconstruct the vehicle centerline, which, in conjunction with an inflated binary occupancy map, facilitates static obstacle collision avoidance functions. Simulation study and animation are set up to test the efficacy of the approach, and the proposed algorithm proves to consistently provide kinematically feasible trajectories that are also collision-free.

Problem

• Develops a modified Hybrid A* algorithm for automated reverse parking
• Ensures collision-free paths in tight spaces using a kinematic vehicle model
• Generates feasible trajectories by combining motion primitives and obstacle avoidance

Innovation

• Modified Hybrid A* algorithm for collision-free path-planning
• Kinematic single-track model for low-speed vehicle motion
• Inflated occupancy map with vehicle centerline for obstacle avoidance