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PC-Diffuser introduces a safety augmentation framework for diffusion-based trajectory planners that integrates a certifiable, path-consistent barrier function directly into the denoising loop. This approach uses a capsule-distance barrier function for collision risk assessment, converts waypoints into dynamically feasible motion, and applies a path-consistent safety filter to eliminate constraint violations while preserving the diffusion model's intended path geometry. By iteratively injecting safety-consistent corrections at each denoising step, PC-Diffuser achieves context-aware safeguarding, improving the reliability of diffusion-based planners in autonomous driving scenarios.
Guaranteeing safety in diffusion-based trajectory planning is now possible by embedding a certifiable barrier function directly into the denoising loop, ensuring forward invariance and preserving the learned path geometry.
Autonomous driving in complex traffic requires planners that generalize beyond hand-crafted rules, motivating data-driven approaches that learn behavior from expert demonstrations. Diffusion-based trajectory planners have recently shown strong closed-loop performance by iteratively denoising a full-horizon plan, but they remain difficult to certify and can fail catastrophically in rare or out-of-distribution scenarios. To address this challenge, we present PC-Diffuser, a safety augmentation framework that embeds a certifiable, path-consistent barrier-function structure directly into the denoising loop of diffusion planning. The key idea is to make safety an intrinsic part of trajectory generation rather than a post-hoc fix: we enforce forward invariance along the rollout while preserving the diffusion model's intended path geometry. Specifically, PC-Diffuser (i) evaluates collision risk using a capsule-distance barrier function that better reflects vehicle geometry and reduces unnecessary conservativeness, (ii) converts denoised waypoints into dynamically feasible motion under a kinematic bicycle model, and (iii) applies a path-consistent safety filter that eliminates residual constraint violations without geometric distortion, so the corrected plan remains close to the learned distribution. By injecting these safety-consistent corrections at every denoising step and feeding the refined trajectory back into the diffusion process, PC-Diffuser enables iterative, context-aware safeguarding instead of post-hoc repair...
