Ambulance Detection and Priority Passage at Urban Intersections Using Transfer Learning and Explainable AI

Static traffic signal timings often cause severe delays for emergency vehicles, including ambulances at junctions in urban areas, putting lives at risk. To highlight this, the present study proposes an intelligent traffic control system that dynamically adjusts traffic signals based on real-time monitoring. The system employs a yolov8-based deep learning model fine-tuned through transfer learning for ambulance detection from live video. At an Intersection over Union (IoU) threshold of 0.5, the model achieves a mean Average Precision (mAP) of 0.860. To ensure continuous tracking, NORFair tracking is implemented to ensure constant detection across frames. Additionally, to improve explainability and, the frame incorporates Local Interpretable Model-Agnostic Explanation (LIME), providing visual signals into the model decision-making process. Once an ambulance is detected, the system instantly triggers a green-light activation for the ambulance's lane, enabling quick emergency response. Unlike conventional systems with fixed signal timing, this approach enables smart and adaptive traffic management in urban environment. However, the system's shortcomings in low-visibility situations, such as at night or in fog, despite its encouraging results, highlight the need for incorporating images taken at night and in foggy weather into the dataset.