DriftingPlanner: Drifting Generative Models for One-Step Trajectory Planning in End-to-End Autonomous Driving
Overview
This research presents DriftingPlanner, a trajectory planner that employs drifting generative models to achieve one-step inference in end-to-end autonomous driving. The method trains a generator to follow an attraction-repulsion field between generated and ground-truth samples per scene, avoiding the blending of plausible maneuvers into invalid trajectories common in coordinate regression. On NAVSIM navtest, DriftingPlanner achieves a state-of-the-art 93.75 PDMS score. With single-step inference, its drifting decoder is within 0.13 PDMS of the best diffusion or flow matching settings that require ten inference steps.
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Reported quantity · PDMS score: 93.75 other · reported trials
“ched pass, and averaging each antithetic pair cancels the linear part of the displacement from the shell center. On NAVSIM navtest, DriftingPlanner reaches a state-of-the-art 93.75 PDMS, and with single-step inference its drifting decoder comes within 0.13 PDMS of the best diffusion or flow matching setting, which needs ten inference steps.”
Exact source · revision 1Source owner not reported
Reported quantity · PDMS difference: 0.13 other · Basis not reported
“ched pass, and averaging each antithetic pair cancels the linear part of the displacement from the shell center. On NAVSIM navtest, DriftingPlanner reaches a state-of-the-art 93.75 PDMS, and with single-step inference its drifting decoder comes within 0.13 PDMS of the best diffusion or flow matching setting, which needs ten inference steps.”
Exact source · revision 1Source owner not reported
Reported quantity · Robot trajectories: 1 trajectories · trajectory count
“arXiv:2611.01283v1 Announce Type: cross Abstract: End-to-end planners decode the ego trajectory either by coordinate regression, which is fast but blends plausible maneuvers into one trajectory that may follow none of them, or by diffusion and flow matching, which model the trajectory distribution but need several inference steps. Drifting models move this iteration into training: a one-step generator learns to follo”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalDriftingPlanner: Drifting Generative Models for One-Step Trajectory Planning in End-to-End Autonomous Driving
This research presents DriftingPlanner, a trajectory planner that employs drifting generative models to achieve one-step inference in end-to-end autonomous driving. The method trains a generator to follow an attraction-repulsion field between generated and ground-truth samples per scene, avoiding the blending of plausible maneuvers into invalid trajectories common in coordinate regression. On NAVSIM navtest, DriftingPlanner achieves a state-of-the-art 93.75 PDMS score. With single-step inference, its drifting decoder is within 0.13 PDMS of the best diffusion or flow matching settings that require ten inference steps.
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