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arXiv · 2410.12673

MambaBEV: An EV-based 3D detection model with Mamba2

Zihan You·Ni Wang·Hao Wang·Qichao Zhao·Jinxiang Wang
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Abstract

Accurate 3D object detection in autonomous driving relies on Bird's Eye View (BEV) perception and effective temporal fusion. However, existing fusion strategies based on convolutional layers or deformable self-attention struggle to model global context in BEV space, leading to reduced accuracy for large objects.To address this limitation, we propose MambaBEV, a novel BEV-based 3D object detection model that leverages Mamba2, an advanced state-space model (SSM) optimized for long-sequence processing. Our key contribution is TemporalMamba, a temporal fusion module that enhances global context modeling through a BEV feature discrete rearrangement mechanism tailored for sequential processing. In addition, we introduce a Mamba-based DETR head to improve multi-object representation. Evaluations on the nuScenes dataset demonstrate that MambaBEV-base achieves 51.7% NDS and an 42.7% mAP. Furthermore, evaluation within an end-to-end autonomous driving paradigm validates its effectiveness in motion forecasting and planning.These results highlight the potential of state-space models for improving global context understanding and large-object detection in autonomous driving perception systems.

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Zihan You, Ni Wang, Hao Wang, Qichao Zhao, Jinxiang Wang. 2024-10-16. MambaBEV: An EV-based 3D detection model with Mamba2. https://arxiv.org/abs/2410.12673

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