用大模型动态调度传感器，让自动驾驶更省力又智能。
PRAM-R: A Perception-Reasoning-Action-Memory Framework with LLM-Guided Modality Routing for Adaptive Autonomous Driving
- 大模型根据环境自动选传感器并分配权重，降低冗余计算。
- 实测减少6.22%传感器使用量，记忆召回率提升20%。
- 适合追求高效自适应的自动驾驶系统研发者。
多模态感知可提升自动驾驶鲁棒性，但持续全开传感器会带来不必要的计算开销。本文提出PRAM-R框架，融合感知-推理-行动-记忆机制，并引入大模型引导的模态路由实现自适应控制。该框架采用异步双环设计：快速反应环负责感知与控制，慢速思辨环执行基于推理的模态选择与记忆更新。大模型路由器结合环境上下文与传感器诊断信息，动态选择并加权模态；分层记忆模块保障时序一致性，支持长期适应。我们进行两阶段评估：(1) 合成压力测试用于稳定性分析，(2) 在nuScenes数据集上进行真实场景验证。合成测试表明，通过滞回机制稳定化，模态切换振荡减少87.2%。真实场景下，nuScenes验证显示，模态使用量降低6.22%，记忆召回率提升20%，同时在复杂城市场景中轨迹精度与全模态基线相当。结果表明，大模型增强的分层记忆架构可在自动驾驶中实现高效且自适应的多模态感知。
原文摘要 · Abstract (English)
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.
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