PRIME: Perception Feedback with Situational Memory Embeddings in VLA Models
Abstract
Current Vision-Language-Action (VLA) models for autonomous driving operate primarily through feedforward inference across the perception--reasoning--planning hierarchy. While modern architectures maintain temporal recurrence within the perceptual module, early perception remains blind to downstream reasoning and navigation goals, processing visual inputs agnostically without prioritizing cues informed by prior decisions. To bridge this gap, this paper introduces PRIME, a learned feedback mechanism that conditions the VLA perceptual queries on a novel Situational Memory. By aggregating latent representations of past perception, reasoning, navigation goals, and predicted behaviors across an L-step window via cross-attention, PRIME enables intent-driven perceptual attention at minimal computational cost, adding only a maximum of 29.7M parameters (0.41% of the 7.3B-parameter base model). Evaluated on the Bench2Drive closed-loop benchmark, PRIME achieves a state-of-the-art Driving Score of 82.47 (+4.73 over ORION) and a Success Rate of 60.00% (+5.38 percentage points), the highest reported Driving Score among published VLAs trained on Think2Drive demonstrations.
Authors 6
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Affiliation as printed
Department of Electrical Engineering and Information Technology , RWTH Aachen University , Germany
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Affiliation as printed
Corporate Research , Robert Bosch GmbH , Renningen , Germany
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Affiliation as printed
Corporate Research , Robert Bosch GmbH , Renningen , Germany
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Affiliation as printed
Corporate Research , Robert Bosch GmbH , Renningen , Germany
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Affiliation as printed
Chair of Radar System Engineering , RWTH Aachen University , Aachen , Germany
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Affiliation as printed
Corporate Research , Robert Bosch GmbH , Renningen , Germany
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