CoMamba: Real-time Cooperative Perception Unlocked with State Space Models

• URL: http://arxiv.org/abs/2409.10699v2
• Date: Fri, 20 Sep 2024 23:09:56 GMT
• Title: CoMamba: Real-time Cooperative Perception Unlocked with State Space Models
• Authors: Jinlong Li, Xinyu Liu, Baolu Li, Runsheng Xu, Jiachen Li, Hongkai Yu, Zhengzhong Tu,
• Abstract summary: CoMamba is a novel cooperative 3D detection framework designed to leverage state-space models for real-time onboard vehicle perception. CoMamba achieves superior performance compared to existing methods while maintaining real-time processing capabilities.
• Score: 39.87600356189242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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