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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
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