提出实时驾驶规划系统，融合规则与语言模型，兼顾速度与可解释性。
RAD-LAD: Rule and Language Grounded Autonomous Driving in Real-Time
- 单次前向传播生成动作规划，推理速度达20Hz，支持闭环实时部署。
- 在nuPlan Test14-Hard和InterPlan上超越现有学习型模型，延迟降低3倍。
- 规则与语言模型结合，实现可靠动作与自适应决策的互补优势。
我们提出LAD，一种基于语言-动作的实时规划器，采用可中断架构，在单次前向传播中以约20 Hz的速度生成运动规划，或以约10 Hz的速度同时输出文本推理与运动规划。LAD具备实时闭环部署能力，相比先前驾驶语言模型延迟降低约3倍，并在nuPlan Test14-Hard和InterPlan数据集上达到基于学习的新基准表现。此外，我们引入RAD，一种针对PDM-Closed结构缺陷设计的规则规划器，在nuPlan Test14-Hard和InterPlan上达到规则类规划器的最先进水平。最终，将RAD与LAD结合形成混合规划系统，验证了规则与学习方法的互补性：规则保障可靠变道等操作，语言模型则实现自适应且可解释的决策。
原文摘要 · Abstract (English)
We present LAD, a real-time language--action planner with an interruptible architecture that produces a motion plan in a single forward pass (~20 Hz) or generates textual reasoning alongside a motion plan (~10 Hz). LAD is fast enough for real-time closed-loop deployment, achieving ~3x lower latency than prior driving language models while setting a new learning-based state of the art on nuPlan Test14-Hard and InterPlan. We also introduce RAD, a rule-based planner designed to address structural limitations of PDM-Closed. RAD achieves state-of-the-art performance among rule-based planners on nuPlan Test14-Hard and InterPlan. Finally, we show that combining RAD and LAD enables hybrid planning that captures the strengths of both approaches. This hybrid system demonstrates that rules and learning provide complementary capabilities: rules support reliable maneuvering, while language enables adaptive and explainable decision-making.
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