实时监测行人并自动延长信号灯，减少老人、残障者被滞留风险。
No Pedestrian Left Behind: Real-Time Detection and Tracking of Vulnerable Road Users for Adaptive Traffic Signal Control
- 用优化的YOLOv12+ByteTrack追踪十字路口行人
- 使行人滞留率从9.10%降至2.60%，安全提升71.4%
- 仅在12.1%的通行周期中触发延时，兼顾效率与安全
当前行人过街信号灯采用固定时长，无法响应行人行为，导致老年人、残障人士或分心行人常在红灯亮起时仍滞留在路中央。本文提出No Pedestrian Left Behind（NPLB）系统，一种实时自适应交通信号控制方案，可监测人行横道内的脆弱道路使用者（VRUs），并在必要时自动延长信号时间。我们在BGVP数据集上评估了五种先进目标检测模型，其中微调后的YOLOv12在[email protected]上达到0.756的最高水平。NPLB将该模型与ByteTrack多目标跟踪算法结合，并引入自适应控制器：当剩余通行时间低于临界阈值时即延长行人相位。通过10,000次蒙特卡洛模拟验证，该系统可使VRU安全性提升71.4%，将滞留率由9.10%降至2.60%，且仅在12.1%的过街周期中需要信号延长。
原文摘要 · Abstract (English)
Current pedestrian crossing signals operate on fixed timing without adjustment to pedestrian behavior, which can leave vulnerable road users (VRUs) such as the elderly, disabled, or distracted pedestrians stranded when the light changes. We introduce No Pedestrian Left Behind (NPLB), a real-time adaptive traffic signal system that monitors VRUs in crosswalks and automatically extends signal timing when needed. We evaluated five state-of-the-art object detection models on the BGVP dataset, with YOLOv12 achieving the highest mean Average Precision at 50% ([email protected]) of 0.756. NPLB integrates our fine-tuned YOLOv12 with ByteTrack multi-object tracking and an adaptive controller that extends pedestrian phases when remaining time falls below a critical threshold. Through 10,000 Monte Carlo simulations, we demonstrate that NPLB improves VRU safety by 71.4%, reducing stranding rates from 9.10% to 2.60%, while requiring signal extensions in only 12.1% of crossing cycles.
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