融合规则与学习的混合规划器，提升自动驾驶轨迹生成的泛化与效率。
SAH-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation
- 用双时间尺度神经元动态切换规则与学习模型决策
- 在interPlan数据集上达最新性能，推理延迟几乎无增加
- 适合需要高可靠性和实时响应的自动驾驶系统
可靠的规划是实现自动驾驶的关键。基于规则的规划器效率高但泛化能力差，基于学习的规划器泛化能力强却存在实时性不足和可解释性差的问题。在长尾场景下，这些挑战尤为突出。为此，我们提出面向闭环车辆轨迹生成的场景感知混合规划器（SAH-Drive）。受人类驾驶行为启发，SAH-Drive结合轻量级规则规划器与全面的学习型规划器，采用双时间尺度决策神经元确定最终轨迹。为提升计算效率与鲁棒性，还引入扩散提议数量调节器与轨迹融合模块。实验表明，该方法显著提升了规划系统的泛化能力，在interPlan基准上达到领先性能，同时保持高效计算，未带来明显额外运行开销。
原文摘要 · Abstract (English)
Reliable planning is crucial for achieving autonomous driving. Rule-based planners are efficient but lack generalization, while learning-based planners excel in generalization yet have limitations in real-time performance and interpretability. In long-tail scenarios, these challenges make planning particularly difficult. To leverage the strengths of both rule-based and learning-based planners, we proposed the Scenario-Aware Hybrid Planner (SAH-Drive) for closed-loop vehicle trajectory planning. Inspired by human driving behavior, SAH-Drive combines a lightweight rule-based planner and a comprehensive learning-based planner, utilizing a dual-timescale decision neuron to determine the final trajectory. To enhance the computational efficiency and robustness of the hybrid planner, we also employed a diffusion proposal number regulator and a trajectory fusion module. The experimental results show that the proposed method significantly improves the generalization capability of the planning system, achieving state-of-the-art performance in interPlan, while maintaining computational efficiency without incurring substantial additional runtime.
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