No Pedestrian Left Behind: Real-Time Detection and Tracking of Vulnerable Road Users for Adaptive Traffic Signal Control
TLDR
This paper introduces NPLB, an adaptive traffic signal system that detects and tracks VRUs to extend crossing times, boosting safety.
Key contributions
- Introduces NPLB, a real-time adaptive traffic signal system for vulnerable road users (VRUs).
- Integrates fine-tuned YOLOv12 object detection with ByteTrack for VRU monitoring.
- Automatically extends pedestrian signal phases when VRUs are detected in crosswalks.
- Simulations show NPLB improves VRU safety by 71.4%, reducing stranding rates to 2.60%.
Why it matters
Current fixed-time signals often strand vulnerable pedestrians, posing safety risks. This system offers a crucial solution by dynamically adjusting signal timing, significantly enhancing VRU safety and reducing stranding without excessive signal extensions.
Original 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.
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