GEMINUS用双感知路由实现端到端自动驾驶的自适应与鲁棒性。
GEMINUS: Dual-aware Global and Scene-Adaptive Mixture-of-Experts for End-to-End Autonomous Driving
- 引入全局专家与场景自适应专家组，通过双感知路由动态激活。
- 在Bench2Drive上达成最高驾驶得分与成功率，仅需单目视觉输入。
- 适合追求高适应性与鲁棒性的自动驾驶系统研发者。
端到端自动驾驶需要在复杂多变的交通环境中具备自适应和鲁棒性。然而，现有单模式规划方法试图学习统一策略，难以掌握多样化的驾驶技能以应对不同场景。为此，本文提出GEMINUS，一种基于全局专家与场景自适应专家组的混合专家（Mixture-of-Experts）端到端自动驾驶框架，配备双感知路由器。全局专家在全量数据集上训练，具备稳健性能；场景自适应专家在对应场景子集上训练，实现自适应表现。双感知路由器同时考虑场景级特征与路由不确定性，动态激活专家模块。通过双感知路由器有效耦合全局专家与场景自适应专家组，GEMINUS在多样化场景中兼具自适应性与鲁棒性。GEMINUS在Bench2Drive闭环基准测试中优于现有方法，驾驶得分与成功率达到当前最优水平，即使仅使用单目视觉输入。代码已开源：https://github.com/newbrains1/GEMINUS。
原文摘要 · Abstract (English)
End-to-end autonomous driving requires adaptive and robust handling of complex and diverse traffic environments. However, prevalent single-mode planning methods attempt to learn an overall policy while struggling to acquire diversified driving skills to handle diverse scenarios. Therefore, this paper proposes GEMINUS, a Mixture-of-Experts end-to-end autonomous driving framework featuring a Global Expert and a Scene-Adaptive Experts Group, equipped with a Dual-aware Router. Specifically, the Global Expert is trained on the overall dataset, possessing robust performance. The Scene-Adaptive Experts are trained on corresponding scene subsets, achieving adaptive performance. The Dual-aware Router simultaneously considers scenario-level features and routing uncertainty to dynamically activate expert modules. Through the effective coupling of the Global Expert and the Scene-Adaptive Experts Group via the Dual-aware Router, GEMINUS achieves both adaptability and robustness across diverse scenarios. GEMINUS outperforms existing methods in the Bench2Drive closed-loop benchmark and achieves state-of-the-art performance in Driving Score and Success Rate, even with only monocular vision input. The code is available at https://github.com/newbrains1/GEMINUS.
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