Multimodal perception enables robust autonomous driving but incurs unnecessary computational cost when all sensors remain active. This paper presents PRAM-R, a unified Perception-Reasoning-Action-Memory framework with LLM-Guided Modality Routing for adaptive autonomous driving. PRAM-R adopts an asynchronous dual-loop design: a fast reactive loop for perception and control, and a slow deliberative loop for reasoning-driven modality selection and memory updates. An LLM router selects and weights modalities using environmental context and sensor diagnostics, while a hierarchical memory module preserves temporal consistency and supports long-term adaptation. We conduct a two-stage evaluation: (1) synthetic stress tests for stability analysis and (2) real-world validation on the nuScenes dataset. Synthetic stress tests confirm 87.2% reduction in routing oscillations via hysteresis-based stabilization. Real-world validation on nuScenes shows 6.22% modality reduction with 20% memory recall while maintaining comparable trajectory accuracy to full-modality baselines in complex urban scenarios. Our work demonstrates that LLM-augmented architectures with hierarchical memory achieve efficient, adaptive multimodal perception in autonomous driving.

翻译：多模态感知能够实现鲁棒的自动驾驶，但当所有传感器保持激活时会产生不必要的计算成本。本文提出PRAM-R，一种用于自适应自动驾驶的、具备LLM引导模态路由的统一感知-推理-行动-记忆框架。PRAM-R采用异步双环路设计：一个用于感知与控制的快速反应环路，以及一个用于推理驱动的模态选择与记忆更新的慢速决策环路。LLM路由器利用环境上下文与传感器诊断信息进行模态选择与加权，而分层记忆模块则保持时间一致性并支持长期适应。我们进行了两阶段评估：（1）用于稳定性分析的合成压力测试，以及（2）在nuScenes数据集上的真实场景验证。合成压力测试证实，基于滞后的稳定机制可将路由振荡降低87.2%。在nuScenes上的真实场景验证表明，在复杂城市场景中，在保持与全模态基线相当的轨迹精度的同时，实现了6.22%的模态减少与20%的记忆召回率。我们的工作证明，具备分层记忆的LLM增强架构能够在自动驾驶中实现高效、自适应的多模态感知。