Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving

The Diffusion Forcing Planner (DFP) introduces a novel diffusion-based framework for autonomous driving that addresses temporal inconsistency in motion planning. By decomposing trajectories into history, current, and future segments with independent noise levels, DFP utilizes classifier-free guidance for controlled future sampling, leading to enhanced stability and adaptability in dynamic environments. This approach demonstrates competitive performance on the nuPlan benchmark, offering practitioners a method to produce more reliable and context-aware motion plans in real-time driving scenarios.