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arXiv Computer Vision By Erik Deinzer, Naya Baslan, Luca Paparusso, Narunas Vaskevicius, Peter Knott, Luigi Palmieri

PRIME: Perception Feedback with Situational Memory Embeddings in VLA Models

Read the original on arXiv Computer Vision →

PRIME introduces a perception feedback mechanism for Vision‑Language‑Action models in autonomous driving, conditioning perceptual queries on a Situational Memory that aggregates past perception, reasoning, navigation goals, and predicted behaviors via cross‑attention. This approach adds only 29.7 M parameters (0.41 % of a 7.3 B‑parameter base model) and enables intent‑driven perceptual attention at minimal computational cost. On the Bench2Drive closed‑loop benchmark, PRIME achieves a state‑of‑the‑art Driving Score of 82.47 and a Success Rate of 60.00 %, outperforming prior models such as ORION.

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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