No Pedestrian Left Behind: Real-Time Detection and Tracking of Vulnerable Road Users for Adaptive Traffic Signal Control
2026-04-28 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial IntelligenceRobotics
AI summaryⓘ
The authors created a smart traffic light system called No Pedestrian Left Behind (NPLB) that watches for people crossing the road and extends the green light if someone might get stuck. They tested several computer vision models and used YOLOv12 to detect pedestrians accurately. Their system tracks pedestrians in real time and decides when to make the crossing time longer. Simulations showed that NPLB reduced the chances of people being stranded by over 70%, while only slightly increasing the number of longer crossing signals.
pedestrian crossing signalsvulnerable road usersobject detectionYOLOv12multi-object trackingadaptive traffic controlMonte Carlo simulationmean Average Precision (mAP)crosswalk safety
Authors
Anas Gamal Aly, Hala ElAarag
Abstract
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% (mAP@0.5) 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.