Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving
arXiv:2606.11019v1 Announce Type: cross Abstract: Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency. Small perturbations across frames can accumulate into unstable trajectories, degrading comfort and safety in closed-loop driving. Several methods attempt to inject history as a static conditioning signal to stabilize outputs, only to induce the planner to copy historical patterns instead of adapting to environment contexts. To address this limitation, we propose Diffusion Forcing Planner (DFP), a diffusion-based planning framework driven by histor
The increasing sophistication of AI models and the demand for more reliable autonomous systems are driving innovations in motion planning, particularly to address temporal consistency issues that have hampered previous learning-based approaches.
This development proposes a novel diffusion-based planning framework that could significantly improve the safety and stability of autonomous driving, thereby accelerating its commercial deployment and expanding its practical applications.
Traditional learning-based motion planners, prone to temporal inconsistency, are being challenged by new methods that offer more stable and adaptive trajectory planning through advanced AI architectures like diffusion models.
- · Autonomous vehicle developers
- · AI research institutions
- · Ride-sharing companies
- · Logistics and delivery services
- · Developers of less robust planning algorithms
- · Companies reliant on human-driven transport
Increased safety and reliability of autonomous driving systems become more achievable.
Faster adoption and broader societal integration of self-driving technology across multiple sectors.
Reduced traffic accidents and improved urban mobility leading to significant economic and social restructuring.
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Read at arXiv cs.AI