V2X-M2C: Efficient Multi-Module Collaborative Perception with Two Connections
<|reference_start|>V2X-M2C: Efficient Multi-Module Collaborative Perception with Two Connections: In this paper, we investigate improving the perception performance of autonomous vehicles through communication with other vehicles and road infrastructures. To this end, we introduce a collaborative perception model $\textbf{V2X-M2C}$, consisting of multiple modules, each generating inter-agent complementary information, spatial global context, and spatial local information. Inspired by the question of why most existing architectures are sequential, we analyze both the $\textit{sequential}$ and $\textit{parallel}$ connections of the modules. The sequential connection synergizes the modules, whereas the parallel connection independently improves each module. Extensive experiments demonstrate that V2X-M2C achieves state-of-the-art perception performance, increasing the detection accuracy by 8.00% to 10.87% and decreasing the FLOPs by 42.81% to 52.64%.<|reference_end|>
@article{bae2024parcon:,
    title={ParCon: Noise-Robust Collaborative Perception via Multi-module Parallel
  Connection},
    author={Hyunchul Bae, Minhee Kang, Heejin Ahn},
    journal={arXiv preprint arXiv:2407.11546},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11546},
    primaryClass={cs.CV}
}