让自动驾驶更懂复杂路况，动态选择专家模型并考虑车辆互动。
Scene-Adaptive Motion Planning with Explicit Mixture of Experts and Interaction-Oriented Optimization
- 用场景路由动态选专家，应对不同路况
- 多模态预测提升规划准确率，优于现有模型
- 适合需要高安全性的智能驾驶系统研究者
尽管自动驾驶技术已发展十余年，复杂城市环境中的轨迹规划仍面临诸多挑战，包括轨迹的多模态特性难以建模、单一专家模型难以应对多样场景、对环境交互考虑不足等问题。为此，本文提出EMoE-Planner，引入三项创新：首先，通过共享场景路由器实现显式混合专家（Explicit MoE）机制，根据场景信息动态选择专用专家；其次，利用场景特定查询提供多模态先验，引导模型聚焦目标区域；最后，通过考虑自车与其他交通参与者之间的交互关系，优化预测模型与损失函数设计。在Nuplan数据集上与最先进方法进行对比实验，仿真结果表明，本模型在几乎所有测试场景中均持续优于当前SOTA方法。本模型是首个在几乎全部Nuplan闭环仿真中表现超越规则基算法的纯学习型模型。
原文摘要 · Abstract (English)
Despite over a decade of development, autonomous driving trajectory planning in complex urban environments continues to encounter significant challenges. These challenges include the difficulty in accommodating the multi-modal nature of trajectories, the limitations of single expert model in managing diverse scenarios, and insufficient consideration of environmental interactions. To address these issues, this paper introduces the EMoE-Planner, which incorporates three innovative approaches. Firstly, the Explicit MoE (Mixture of Experts) dynamically selects specialized experts based on scenario-specific information through a shared scene router. Secondly, the planner utilizes scene-specific queries to provide multi-modal priors, directing the model's focus towards relevant target areas. Lastly, it enhances the prediction model and loss calculation by considering the interactions between the ego vehicle and other agents, thereby significantly boosting planning performance. Comparative experiments were conducted on the Nuplan dataset against the state-of-the-art methods. The simulation results demonstrate that our model consistently outperforms SOTA models across nearly all test scenarios. Our model is the first pure learning model to achieve performance surpassing rule-based algorithms in almost all Nuplan closed-loop simulations.
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