让自动驾驶车实时识别施工区限速，提升动态路况安全
Vision-Language Work Zone Intelligence for Safety-Critical Speed Regulation of Mixed-Autonomy Vehicles in Dynamic Environments
- 融合目标检测与语义验证，动态滤波减少误触发
- 施工区识别召回率达96.5%，限速识别准确率95.45%
- 可部署于低成本嵌入式设备，适合实际道路应用
临时施工区限速标志视觉不一致且常未收录于数字地图，对人工驾驶与自动驾驶系统构成安全隐患。本文提出一种实时车载感知流程，可检测活跃施工区、识别临时限速，并输出符合法规的施工区状态与速度值，用于驾驶员提醒或下游自动控制。系统融合目标检测与语义验证，采用时序平滑与带滞后的状态转换机制，有效降低动态场景中的误激活与闪烁现象，全程运行于低成本嵌入式硬件。在ROADWork数据集标注子集（490个序列）上评估，施工区事件级召回率为96.5%，精度为68.7%；在35分钟自采驾驶数据上，限速识别精度达95.45%，召回率为53.85%，无错误分类，仅1个误报。结果表明，该方法可在车载感知层面直接实现施工区限速认知，无需依赖地图或基础设施。源代码已开源：https://github.com/Mi3-Lab/workzone
原文摘要 · Abstract (English)
Temporary work-zone speed limits are communicated through visually inconsistent signage and are often missing from digital maps, creating safety risks for human drivers and automated vehicle systems. We present a real-time, onboard perception pipeline that detects active work zones, recognizes associated temporary speed limits, and outputs a law-aware work-zone state and speed value suitable for driver alerts or downstream automated control. The system fuses object detections with semantic verification and temporally smoothed, hysteresis-based state transitions to reduce false activations and flicker in dynamic scenes, and runs fully on low-cost embedded hardware. Evaluated manually on a annotated subset of the ROADWork dataset (490 sequences), the system achieves inside-work-zone event-level recall of 96.5% and event-level precision of 68.7%. Speed-limit recognition evaluated on 35 minutes of in-house driving data attains 95.45% precision and 53.85% recall, with no incorrect speed classifications and a single false positive. These results demonstrate a practical, scalable approach for grounding work-zone speed awareness directly in onboard perception rather than maps or infrastructure. We release our source code for the proposed system pipeline on our GitHub repository: https://github.com/Mi3-Lab/workzone
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