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arXiv Computer Vision By Zhao Yang, Zezhong Qian, Yinan Shi, Longjun Liu, Yawei Jueluo

GRID-Planner: Graph-Structured Relational Reward Shaping for Diffusion Planning

Read the original on arXiv Computer Vision →

GRID‑Planner introduces a graph‑structured relational reward shaping framework for diffusion‑based autonomous driving planning. By integrating graph‑aware importance weighting, a time‑indexed relational reward surrogate, and an advantage‑weighted diffusion objective with on‑policy self‑distillation, it forms a rollout‑return‑advantage‑update loop that eliminates explicit likelihoods. Experiments on nuPlan and interPlan demonstrate significant reductions in collision rate, lane deviation, and jerk, along with a 3.4% improvement in overall planning score.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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