ClearPath-AI is a research-oriented project that explores the use of computer vision and real-time decision systems to automatically prioritize emergency vehicles at traffic intersections.
The system detects emergency vehicles using a deep learning object detection model and dynamically controls traffic signals to create a green corridor, reducing response time and improving the effectiveness of emergency services in urban environments.
Urban traffic congestion is a major factor contributing to delays in emergency response.
Traditional traffic signal systems are static or semi-adaptive and do not account for real-time emergency scenarios.
Even small delays can have life-critical consequences in medical and disaster-response situations.
This project is motivated by the need for an intelligent, automated, and vision-based traffic control mechanism that can react to emergency vehicles in real time without relying on manual intervention or special hardware in vehicles.
To design a system that:
- Can detect emergency vehicles using roadside camera feeds
- Can make real-time decisions at traffic intersections
- Can dynamically override traffic signals to prioritize emergency vehicles
- While remaining scalable and deployable in smart-city environments
The proposed system uses:
- A vision-based object detection model (YOLOv8) for identifying emergency vehicles
- A rule-based decision engine for traffic signal control
- A real-time video processing pipeline to ensure low-latency response
The system temporarily alters the signal phase of the intersection in favor of the detected emergency vehicle and restores normal operation after it passes.
Traffic Camera
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YOLOv8 Detection Model
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Emergency Vehicle Classification
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Decision & Control Logic
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Traffic Signal Controller