1. AI Road Divider Mechanism Ensuring Ambulance Passage

In urban environments, traffic congestion remains a critical obstacle for emergency vehicles such as ambulances, often leading to life-threatening delays. This paper offers a cutting edge, entirely software based solution that uses computer vision and artificial intelligence to dynamically regulate traffic on the roads, guaranteeing the smooth and effecti ve flow of ambulances without requiring any changes to the physical infrastructure. The thre e main modules of the suggested system a re decision detection, and lane detection. To detect and track lane violations, the lane identification module uses a Convolutional Neu ral Network (CNN) model that has been refined on actual road datasets. boundaries in real-time. Simultaneously, the ambulance detection module leverages the YOLOv5 object detection algorithm, trained on a custom ambulance dataset, to detect emergency vehicles within the monitored area with over 95% accuracy.Upon detecting an ambulance, the decision-making module uses a rule-based AI mechanism to dynamically alter virtual road dividers, temporarily prioritizing lanes to clear the ambulance's path. Extensive testing under simulated and real-world traffic conditions demonstrate the system's ability to reduce response times by 40%, making it highly scalable and cost-effective due to its software-only design. This work highlights the potential of AI-driven traffic control systems in enhancing public safety and presents a roadmap f or future integration with live traffic management systems.