用自然语言定义传感器触发条件，实现智能选录多模态数据。
A Domain-Specific Language for LLM-Driven Trigger Generation in Multimodal Data Collection
- 通过自然语言转译为可验证的领域专用语言程序，实现条件触发。
- 在车载与机器人任务中，生成一致性更高、延迟更低，检测性能相当。
- 适合边缘设备上资源受限场景下的实时多模态数据采集需求。
数据驱动系统依赖任务相关数据，但现有数据收集流程仍为被动且无差别。持续记录多模态传感器流导致高存储开销并捕获无关数据。本文提出一种声明式框架，支持基于用户意图的本地化数据收集，可根据高层请求有选择地采集多模态传感器数据。该框架结合自然语言交互与形式化定义的领域特定语言（DSL）。大语言模型将用户需求转化为可验证、可组合的DSL程序，用于定义跨异构传感器（包括摄像头、激光雷达和系统遥测）的条件触发器。在车载与机器人感知任务上的实证评估表明，基于DSL的方法在生成一致性上优于无约束代码生成，执行延迟更低，同时保持相近的检测性能。结构化抽象支持模块化触发组合，并可在资源受限的边缘平台并发部署。该方法将被动日志记录替换为可验证、意图驱动的实时多模态数据收集机制。
原文摘要 · Abstract (English)
Data-driven systems depend on task-relevant data, yet data collection pipelines remain passive and indiscriminate. Continuous logging of multimodal sensor streams incurs high storage costs and captures irrelevant data. This paper proposes a declarative framework for intent-driven, on-device data collection that enables selective collection of multimodal sensor data based on high-level user requests. The framework combines natural language interaction with a formally specified domain-specific language (DSL). Large language models translate user-defined requirements into verifiable and composable DSL programs that define conditional triggers across heterogeneous sensors, including cameras, LiDAR, and system telemetry. Empirical evaluation on vehicular and robotic perception tasks shows that the DSL-based approach achieves higher generation consistency and lower execution latency than unconstrained code generation while maintaining comparable detection performance. The structured abstraction supports modular trigger composition and concurrent deployment on resource-constrained edge platforms. This approach replaces passive logging with a verifiable, intent-driven mechanism for multimodal data collection in real-time systems.
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