CoMamba: Real-time Cooperative Perception Unlocked with State Space Models
<|reference_start|>CoMamba: Real-time Cooperative Perception Unlocked with State Space Models: 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.<|reference_end|>
@article{li2024comamba:,
    title={CoMamba: Real-time Cooperative Perception Unlocked with State Space
  Models},
    author={Jinlong Li, Xinyu Liu, Baolu Li, Runsheng Xu, Jiachen Li, Hongkai Yu,
  Zhengzhong Tu},
    journal={arXiv preprint arXiv:2409.10699},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10699},
    primaryClass={cs.CV}
}