No Pedestrian Left Behind: Real-Time Detection and Tracking of Vulnerable Road Users for Adaptive Traffic Signal Control

TL;DR

The paper presents NPLB, a real-time adaptive traffic signal system that enhances safety for vulnerable pedestrians by monitoring and extending crossing signals based on real-time detection and tracking.

Contribution

Introducing NPLB, a novel system combining state-of-the-art detection, tracking, and adaptive control to improve pedestrian safety at crossings.

Findings

NPLB reduces pedestrian stranding rates by 71.4%.

YOLOv12 achieved the highest detection accuracy on BGVP dataset.

NPLB requires signal extension in only 12.1% of cycles.

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.