DriftParking: Trajectory Modeling via Drifting Field for End-to-End Automated Parking
TL;DR
This work proposes DriftParking, a one-step trajectory generation framework that reconstructs the drifting-field paradigm for high-precision conditional trajectory generation, and replaces distribution-level attraction with conditional one-to-one attraction toward the paired expert trajectory, introduce expert-centered constructive repulsion, and adaptively attenuate repulsion near convergence.
Abstract
Automated parking requires generating complete and executable trajectories in highly constrained spaces with low tolerance for goal pose error. Existing end-to-end parking methods struggle to jointly achieve inference efficiency, trajectory quality, and precise endpoint alignment, while conventional imitation objectives provide limited supervision on structured deviations from expert maneuver geometry. We propose DriftParking, a one-step trajectory generation framework that reconstructs the drifting-field paradigm for high-precision conditional trajectory generation. Specifically, we replace distribution-level attraction with conditional one-to-one attraction toward the paired expert trajectory, introduce expert-centered constructive repulsion, and adaptively attenuate repulsion near convergence. We further formulate trajectory generation in an endpoint-residual space by decomposing each trajectory into a start-to-goal baseline and a learnable residual, turning endpoint alignment into a representation-level structural constraint on the supervision target while providing a structured space for repulsive supervision. DriftParking achieves state-of-the-art performance across all evaluation metrics. Closed-loop on-vehicle experiments across diverse parking scenarios further show a 97% parking success rate, demonstrating strong zero-shot generalization.