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An Enhanced Hybrid Astar Path Planning Algorithm Using Guided Search and Corridor Constraints.

Na Che^1,2,3, Xianwei Zeng¹, Jian Zhao^1,2,3

¹College of Computer Science and Technology, Changchun University, Changchun 130022, China.

Sensors (Basel, Switzerland)

|January 28, 2026

Summary

This study introduces GCHybridA*, an improved path planning algorithm for autonomous driving. It enhances safety and efficiency by using Voronoi diagrams and safe corridors, significantly reducing search space and ensuring collision-free paths.

Keywords:

HybridA* autonomous vehicles guided search path planning safe corridor

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Area of Science:

Robotics and Autonomous Systems
Artificial Intelligence
Computational Geometry

Background:

Traditional HybridA* algorithms struggle with large search spaces, computational inefficiency, and safety concerns in complex environments.
Path planning is critical for autonomous driving systems, requiring efficient and safe navigation strategies.

Purpose of the Study:

To propose an improved HybridA* algorithm (GCHybridA*) addressing the limitations of traditional methods.
To enhance the efficiency and safety of global path planning in static environments for autonomous vehicles.

Main Methods:

Constructing a Voronoi path away from obstacles and smoothing it to reduce ineffective node expansion.
Generating a safe corridor to restrict path search, ensuring path safety.
Implementing an adaptive step-size mechanism to balance search efficiency and path quality.

Main Results:

The GCHybridA* algorithm achieved an average reduction of 83.7% in node expansions compared to the conventional HybridA* algorithm.
Zero potential collision points were maintained across all tested maps, demonstrating enhanced safety.
Significant improvements in computational efficiency and path quality were observed.

Conclusions:

GCHybridA* offers an innovative and robust solution for efficient and safe path planning in autonomous driving.
The algorithm provides safety guarantees for navigation in static environments.
This approach effectively overcomes the limitations of traditional path planning methods.