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The study aims to develop a more efficient path planning algorithm for autonomous vehicles that adapts to various driving scenarios.

February 5, 2026ElectronicsOpen Access

Optimized PAB-RRT Algorithm for Autonomous Vehicle Path Planning in Complex Scenarios

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The study aims to develop a more efficient path planning algorithm for autonomous vehicles that adapts to various driving scenarios.
Proposed the PAB-RRT algorithm integrating key features like goal bias, adaptive step size, and bidirectional exploration.
Conducted comparative simulations against traditional RRT and its improved variants.
Evaluated algorithm performance in static and dynamic multi-obstacle scenarios.
PAB-RRT achieved significantly fewer iterations and reduced computation time than traditional RRT.
Path length generated by PAB-RRT was 7.2% shorter in static scenarios compared to traditional RRT.
In dynamic scenarios, PAB-RRT maintained stable path lengths with reduced iterations.

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Comparative evaluation demonstrates improved path planning efficiency in autonomous vehicles, suggesting enhanced safety and comfort.

Wang et al. (2026) studied this question.

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