AINeutralarXiv – CS AI · 6h ago6/10
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Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving
Researchers propose Diffusion Forcing Planner (DFP), a new diffusion-based motion planning framework for autonomous driving that addresses temporal inconsistency in learning-based planners. By decomposing trajectories into history, current, and future segments with independent noise levels and applying annealed guidance, DFP produces more stable and controllable driving plans while avoiding the tendency to simply copy historical patterns.