用状态空间模型实现车载实时协同感知，效率更高。
CoMamba: Real-time Cooperative Perception Unlocked with State Space Models
- 采用双向状态空间模型替代注意力机制，解决计算复杂度问题。
- 在V2X数据集上检测精度优于现有方法，且保持实时处理能力。
- 适合智能交通中大规模车辆协同感知场景使用。
协同感知系统在提升车联网安全与效率方面至关重要。尽管近年来车联万物（V2X）通信技术在自动驾驶中展现出显著效果，但核心挑战依然存在：如何高效融合不断扩展的联网设备（如车辆与基础设施）产生的高带宽特征。本文提出CoMamba，一种新型的协同3D检测框架，利用状态空间模型实现车载实时感知。相较于以往基于Transformer的先进模型，CoMamba采用双向状态空间模型，避免了注意力机制带来的二次复杂度瓶颈，具备更强可扩展性。在V2X/V2V数据集上的大量实验表明，CoMamba在保持实时处理能力的同时，性能显著优于现有方法。该框架不仅提升了目标检测精度，还大幅降低处理时间，为下一代智能交通网络中的协同感知系统提供了有力解决方案。
原文摘要 · Abstract (English)
Cooperative perception systems play a vital role in enhancing the safety and efficiency of vehicular autonomy. Although recent studies have highlighted the efficacy of vehicle-to-everything (V2X) communication techniques in autonomous driving, a significant challenge persists: how to efficiently integrate multiple high-bandwidth features across an expanding network of connected agents such as vehicles and infrastructure. In this paper, we introduce CoMamba, a novel cooperative 3D detection framework designed to leverage state-space models for real-time onboard vehicle perception. Compared to prior state-of-the-art transformer-based models, CoMamba enjoys being a more scalable 3D model using bidirectional state space models, bypassing the quadratic complexity pain-point of attention mechanisms. Through extensive experimentation on V2X/V2V datasets, CoMamba achieves superior performance compared to existing methods while maintaining real-time processing capabilities. The proposed framework not only enhances object detection accuracy but also significantly reduces processing time, making it a promising solution for next-generation cooperative perception systems in intelligent transportation networks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。