Safe and efficient path planning in parking scenarios presents a significant challenge due to the presence of cluttered environments filled with static and dynamic obstacles. To address this, we propose a novel and computationally efficient planning strategy that seamlessly integrates the predictions of dynamic obstacles into the planning process, ensuring the generation of collision-free paths. Our approach builds upon the conventional Hybrid A star algorithm by introducing a time-indexed variant that explicitly accounts for the predictions of dynamic obstacles during node exploration in the graph, thus enabling dynamic obstacle avoidance. We integrate the time-indexed Hybrid A star algorithm within an online planning framework to compute local paths at each planning step, guided by an adaptively chosen intermediate goal. The proposed method is validated in diverse parking scenarios, including perpendicular, angled, and parallel parking. Through simulations, we showcase our approach's potential in greatly improving the efficiency and safety when compared to the state of the art spline-based planning method for parking situations.
View on arXiv@article{nawaz2025_2504.12616,
title={ Graph-based Path Planning with Dynamic Obstacle Avoidance for Autonomous Parking },
author={ Farhad Nawaz and Minjun Sung and Darshan Gadginmath and Jovin D'sa and Sangjae Bae and David Isele and Nadia Figueroa and Nikolai Matni and Faizan M. Tariq },
journal={arXiv preprint arXiv:2504.12616},
year={ 2025 }
}