AI Smart Ambulance Detection & Priority Traffic Preemption System
Intelligent urban traffic control system detecting emergency ambulances via acoustic siren analysis and YOLO computer vision to automate green light clearance.
### AI Smart Ambulance Detection & Traffic Preemption Engine (FYP Complete)
Emergency ambulances worldwide lose critical survival minutes trapped in urban traffic congestion. This multi-modal AI engineering project combines audio spectral analysis (MFCC + CNN siren classifier) with real-time video detection (YOLOv8 Emergency Vehicle classifier) to trigger dynamic traffic signal phase preemption, granting ambulances uninterrupted green corridors.
#### 🧠 Architecture & Tech Stack:
- **Audio Classifier**: Mel-Frequency Cepstral Coefficients (MFCC) with 1D CNN recognizing emergency sirens with >95% accuracy in noisy traffic
- **Visual Detector**: YOLOv8 nano fine-tuned on ambulance & emergency vehicle datasets (>30 FPS)
- **Traffic Controller Algorithm**: Dynamic queue clearing logic interfacing with hardware traffic lights via GPIO / Serial
- **Web Telemetry**: Real-time Django command dashboard with emergency vehicle GPS map tracking and clearance logs
What's Included in the Package:
- Complete Python & YOLOv8 Source Code
- Pre-trained Siren Audio & Vehicle Detection Models
- Traffic Signal Controller Hardware Logic Code
- Complete 55+ Page IEEE Project Report (DOC & PDF)
- Defense Presentation Slides (PPTX)
- Installation & Run Video Guide
Quick Build & Interfacing Guide:
Unbox and verify all pre-tested sensors, microcontrollers, and wiring components included in your package.
Secure mechanical brackets, servos, and modules to the main frame using the provided mounting hardware.
Interface VCC, GND, and GPIO signal lines according to the verified schematic diagram provided with the kit.
Upload the tested source code via Arduino IDE, Python, or ESP-IDF and verify live sensor readings and actuation.
Key Features
Specifications
Community Questions & Answers
Have a question about compatibility, code, or delivery? Ask our engineering team or see what other Pakistani makers asked.
Sir laptop pe bina GPU k run ho jaye ga for final viva demonstration?
Yes! The YOLOv8 inference is optimized with OpenCV DNN backend to run smoothly on standard Intel Core i5/i7 CPU with 8GB RAM at 25-30 FPS without needing a dedicated gaming GPU.
Assalam o Alaikum, bhai is project ka source code Python 3.10/3.11 pe test kia hua hai? Aur kya IEEE report editable MS Word doc format me mile gi?
Walaikum Assalam Hamza! Yes, 100% verified on Python 3.10 and 3.11. The package includes full Django web app source code, pre-trained YOLO acoustic/vision weights, and a 55+ page IEEE formatted report in both editable MS Word (.docx) and PDF formats, plus PPT defense slides.