New Diffusion-Based Planning Framework Improves Stability in Autonomous Driving Motion Planning
Researchers have developed Diffusion Forcing Planner (DFP), a new machine learning framework designed to address temporal inconsistency problems in autonomous vehicle motion planning. The approach uses history-guided control and independent noise levels for different trajectory segments to prevent small perturbations from accumulating into unstable driving paths. The work demonstrates competitive performance on the nuPlan benchmark while producing more continuous and stable motion plans in complex driving scenarios.
The paper introduces Diffusion Forcing Planner, a diffusion-based framework that tackles a key challenge in learning-based motion planners: temporal inconsistency that can degrade comfort and safety. Previous approaches attempted to stabilize outputs by conditioning on historical data, but this often caused planners to simply copy past patterns rather than adapt to current environmental conditions. DFP addresses this by decomposing trajectories into history, current, and future segments, assigning independent noise levels to each, and jointly denoising them through a heterogeneous diffusion process. During inference, the system applies classifier-free guidance with annealed history to controllably steer future trajectory sampling. Closed-loop evaluation and ablation studies on the nuPlan autonomous driving benchmark demonstrate that DFP achieves competitive performance while producing continuous, stable, and controllable motion plans across complex driving scenarios.
What's missing
The paper does not discuss computational requirements or inference latency, which are critical for real-time autonomous driving applications. Additionally, while nuPlan evaluation is mentioned, comparison details with other state-of-the-art motion planning methods are not provided in the abstract.
What different sources said
- arXiv cs.AICenter
Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making
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