STAMP: Scalable Task- And Model-agnostic Collaborative Perception

ICLR 2025 Conference Submission3152 Authors

Keywords: Autonomous Driving, Collaborative Perception, Domain Adaptation

TL;DR: We propose STAMP, a scalable, task- and model-agnostic collaborative perception framework that enables efficient feature sharing and fusion among heterogeneous agents in autonomous driving

Abstract: Perception is a crucial component of autonomous driving systems. However, single-agent setups often face limitations due to sensor constraints, especially under challenging conditions like severe occlusion, adverse weather, and long-range object detection. Multi-agent collaborative perception (CP) offers a promising solution that enables communication and information sharing between connected vehicles. Yet, the heterogeneity among agents—in terms of sensors, models, and tasks—significantly hinders effective and efficient cross-agent collaboration. To address these challenges, we propose STAMP, a scalable task- and model-agnostic collaborative perception framework tailored for heterogeneous agents. STAMP utilizes lightweight adapter-reverter pairs to transform Bird's Eye View (BEV) features between agent-specific domains and a shared protocol domain, facilitating efficient feature sharing and fusion while minimizing computational overhead. Moreover, our approach enhances scalability, preserves model security, and accommodates a diverse range of agents. Extensive experiments on both simulated (OPV2V) and real-world (V2V4Real) datasets demonstrate that STAMP achieves comparable or superior accuracy to state-of-the-art models with significantly reduced computational costs. As the first-of-its-kind task- and model-agnostic collaborative perception framework, STAMP aims to advance research in scalable and secure mobility systems, bringing us closer to Level 5 autonomy. Our project page is at \href{https://jocular-manatee-91cad0.netlify.app/}{https://jocular-manatee-91cad0.netlify.app} and the code is available at \href{https://anonymous.4open.science/r/STAMP-id}{https://anonymous.4open.science/r/STAMP}.

Primary Area: applications to robotics, autonomy, planning

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Submission Number: 3152