让不同车型的自动驾驶系统共享感知信息，提升复杂路况下的识别能力。
STAMP: Scalable Task And Model-agnostic Collaborative Perception
- 用轻量级适配器在各车之间转换感知特征，实现跨模型协作。
- 实测显示精度媲美顶尖模型，计算开销大幅降低。
- 适合多车协同感知研究者与智能交通系统开发者使用。
感知对自动驾驶至关重要，但单个车辆受限于传感器物理条件，在严重遮挡、恶劣天气和远距离目标检测中表现下降。多车协同感知可缓解此问题，但异构车辆间模型架构差异带来融合挑战。为此，我们提出STAMP——一种可扩展的任务与模型无关的协同感知框架。STAMP采用轻量级适配器-还原器对，实现车载鸟瞰图（BEV）特征在特定模型域与统一协议域间的转换，支持高效特征共享与融合，显著降低计算开销，保障模型安全。在仿真与真实数据集上的实验表明，STAMP性能可达到或超越当前最优模型，且计算成本大幅降低。作为首个任务与模型无关的协同感知框架，STAMP旨在推动面向全自动驾驶的可扩展、安全移动系统发展。项目主页：https://xiangbogaobarry.github.io/STAMP，代码开源：https://github.com/taco-group/STAMP。
原文摘要 · Abstract (English)
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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