STAMP: Scalable Task And Model-agnostic Collaborative Perception

• URL: http://arxiv.org/abs/2501.18616v1
• Date: Fri, 24 Jan 2025 16:27:28 GMT
• Title: STAMP: Scalable Task And Model-agnostic Collaborative Perception
• Authors: Xiangbo Gao, Runsheng Xu, Jiachen Li, Ziran Wang, Zhiwen Fan, Zhengzhong Tu,
• Abstract summary: STAMP is a task- and model-agnostic, collaborative perception pipeline for heterogeneous agents.<n>It minimizes computational overhead, enhances scalability, and preserves model security.<n>As a first-of-its-kind framework, STAMP aims to advance research in scalable and secure mobility systems towards Level 5 autonomy.
• Score: 24.890993164334766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Perception is crucial for autonomous driving, but single-agent perception is often constrained by sensors' physical limitations, leading to degraded performance under severe occlusion, adverse weather conditions, and when detecting distant objects. Multi-agent collaborative perception offers a solution, yet challenges arise when integrating heterogeneous agents with varying model architectures. To address these challenges, we propose STAMP, a scalable task- and model-agnostic, collaborative perception pipeline for heterogeneous agents. STAMP utilizes lightweight adapter-reverter pairs to transform Bird's Eye View (BEV) features between agent-specific and shared protocol domains, enabling efficient feature sharing and fusion. This approach minimizes computational overhead, enhances scalability, and preserves model security. Experiments on simulated and real-world datasets demonstrate STAMP's comparable or superior accuracy to state-of-the-art models with significantly reduced computational costs. As a first-of-its-kind task- and model-agnostic framework, STAMP aims to advance research in scalable and secure mobility systems towards Level 5 autonomy. Our project page is at https://xiangbogaobarry.github.io/STAMP and the code is available at https://github.com/taco-group/STAMP.

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