为自动驾驶感知设计分层解耦的专家混合架构，提升模型适应性与效率。
CBDES MoE: Hierarchically Decoupled Mixture-of-Experts for Functional Modules in Autonomous Driving
- 在功能模块级构建分层解耦的专家混合网络，动态选择最优路径。
- nuScenes上相比最强单专家模型，mAP提升1.6点，NDS提升4.1点。
- 适用于需要高效多模态融合的自动驾驶感知系统，适合追求性能与推理效率平衡的研究者。
基于多传感器特征融合的鸟瞰图（BEV）感知系统已成为端到端自动驾驶的基础。然而，现有方法普遍存在输入适应性差、建模能力受限和泛化性能不足的问题。为此，我们提出一种在功能模块层面构建的分层解耦专家混合架构——计算大脑发展系统专家混合（CBDES MoE）。该架构集成多个结构异构的专家网络，并采用轻量级自注意力路由机制（SAR），实现动态专家路径选择与稀疏、输入感知的高效推理。据我们所知，这是首个在自动驾驶领域以功能模块粒度构建的专家混合框架。在真实世界nuScenes数据集上的大量实验表明，CBDES MoE在3D目标检测任务中持续优于固定单专家基线。相较于最强单专家模型，其mAP提升1.6点，NDS提升4.1点，验证了该方法的有效性与实际优势。
原文摘要 · Abstract (English)
Bird's Eye View (BEV) perception systems based on multi-sensor feature fusion have become a fundamental cornerstone for end-to-end autonomous driving. However, existing multi-modal BEV methods commonly suffer from limited input adaptability, constrained modeling capacity, and suboptimal generalization. To address these challenges, we propose a hierarchically decoupled Mixture-of-Experts architecture at the functional module level, termed Computing Brain DEvelopment System Mixture-of-Experts (CBDES MoE). CBDES MoE integrates multiple structurally heterogeneous expert networks with a lightweight Self-Attention Router (SAR) gating mechanism, enabling dynamic expert path selection and sparse, input-aware efficient inference. To the best of our knowledge, this is the first modular Mixture-of-Experts framework constructed at the functional module granularity within the autonomous driving domain. Extensive evaluations on the real-world nuScenes dataset demonstrate that CBDES MoE consistently outperforms fixed single-expert baselines in 3D object detection. Compared to the strongest single-expert model, CBDES MoE achieves a 1.6-point increase in mAP and a 4.1-point improvement in NDS, demonstrating the effectiveness and practical advantages of the proposed approach.
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