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.

翻译：尽管经过十余年发展，复杂城市场景下的自动驾驶轨迹规划仍面临重大挑战，包括难以适应轨迹的多模态特性、单一专家模型处理多样化场景的局限性，以及对环境交互考量不足等问题。为解决这些难题，本文提出EMoE-Planner规划器，其包含三项创新方法：首先，显式专家混合通过共享场景路由器基于场景特定信息动态选择专业专家；其次，规划器利用场景特定查询提供多模态先验，将模型注意力引导至相关目标区域；最后，通过考虑主车与其他交通参与者之间的交互作用，增强预测模型与损失计算，从而显著提升规划性能。我们在Nuplan数据集上进行了与前沿方法的对比实验，仿真结果表明我们的模型在几乎所有测试场景中均持续优于SOTA模型。本模型是首个在几乎所有Nuplan闭环仿真中性能超越基于规则算法的纯学习模型。