SOURCE-LINKED INTELLIGENCE
DriftParking: Trajectory Modeling via Drifting Field for End-to-End Automated Parking
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 d
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-07T01:42:57.000Z
First collected: 2026-09-20T20:52:10.320Z. This is not the publication date.