同济大学最新！GEMINUS：端到端MoE实现闭环新SOTA，性能大涨近8%~

Core Viewpoint - The article presents GEMINUS, a novel end-to-end autonomous driving framework that integrates a dual-aware mixture of experts (MoE) architecture, achieving state-of-the-art performance in driving score and success rate using monocular vision input [1][2][49]. Summary by Sections Introduction - GEMINUS addresses the limitations of traditional single-modal planning methods in autonomous driving by introducing a framework that combines a global expert and a scene-adaptive experts group, along with a dual-aware router to enhance adaptability and robustness in diverse driving scenarios [1][6]. Background - The article discusses the evolution of end-to-end autonomous driving systems, highlighting the shift from modular approaches to unified models that directly map sensor inputs to control signals, thus reducing engineering workload and leveraging rich sensor information [4][8]. MoE Architecture - The MoE architecture has shown promise in handling complex data distributions, providing fine-grained scene adaptability and specialized behavior generation, which helps mitigate the mode averaging problem prevalent in existing models [5][11]. GEMINUS Framework - GEMINUS consists of a global expert trained on the overall dataset for robust performance and scene-adaptive experts trained on specific scene subsets for adaptability. The dual-aware router dynamically activates the appropriate expert based on scene features and routing uncertainty [6][18]. Experimental Results - GEMINUS outperformed existing methods in the Bench2Drive closed-loop benchmark, achieving a driving score improvement of 7.67% and a success rate increase of 22.06% compared to the original single-expert baseline model [2][36][49]. Ablation Studies - The ablation studies revealed that the scene-aware routing mechanism significantly enhances model performance, while the integration of uncertainty-aware routing and global experts further improves robustness and stability in ambiguous scenarios [40][41]. Conclusion - GEMINUS demonstrates a significant advancement in end-to-end autonomous driving, achieving state-of-the-art performance with monocular vision input and highlighting the importance of tailored MoE frameworks to address the complexities of real-world driving scenarios [49][50].