Graph-based Path Planning with Dynamic Obstacle Avoidance for Autonomous Parking

IEEE Intelligent Vehicles Symposium 2025
Farhad Nawaz*, Minjun Sung, Darshan Gadginmath, Jovin D’sa, Sangjae Bae, David Isele, Nadia Figueroa, Nikolai Matni and Faizan M. Tariq

*farhadn@seas.upenn.edu

Abstract: 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.

A parallel parking scenario with static cars (black), pedestrians, and a moving car (red). The brown car is the ego vehicle. The more transparent images of the red car and the pedestrian denote their respective predictions.

Perpendicular reverse-in parking with one dynamic obstacle