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.

翻译：我们提出了LAD，一种具有可中断架构的实时语言-动作规划器，能够在单次前向传播中（约20赫兹）生成运动规划，或在生成文本推理的同时输出运动规划（约10赫兹）。LAD的速度足以支持实时闭环部署，相较于先前的驾驶语言模型实现了约3倍的延迟降低，同时在nuPlan Test14-Hard和InterPlan基准测试中创下基于学习方法的最新性能。我们还介绍了RAD，一种旨在解决PDM-Closed结构性局限的基于规则的规划器。RAD在nuPlan Test14-Hard和InterPlan的基于规则规划器中取得了最优性能。最后，我们展示了将RAD与LAD相结合，能够实现融合两种方法优势的混合规划。这一混合系统表明，规则与学习提供了互补能力：规则支持稳健的机动操作，而语言则实现了自适应与可解释的决策。