RoadSafe

RoadSafe is an AI application that detects road accidents in real time and automatically alerts emergency services, hospitals, and traffic management to save lives.

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🚀 Inspiration

In a world where road accidents claim thousands of lives daily, delayed emergency response remains a key reason for preventable fatalities. We were inspired to create a system that leverages AI and real-time data to reduce response time and guide bystanders in providing immediate assistance.

🧠 What it does

RoadSafe detects accidents using AI-powered image recognition, classifies their severity, and instantly triggers an emergency response. It notifies nearby hospitals, ambulances, and police while guiding bystanders through a first-aid chatbot. It also tracks ambulance dispatch, updates hospital availability, and informs emergency contacts—all within a single integrated platform.

🛠️ How we built it

We built RoadSafe using:

  • Vite + React + TypeScript for the frontend
  • Tailwind CSS and ShadCN UI for styling
  • Google Maps API for real-time geolocation and routing
  • AI model (CNN) for severity classification
  • Firebase/Supabase for backend services
  • REST APIs for ambulance and alert communication
  • A pre-fed decision-based first aid chatbot All components were integrated through a modular architecture optimized for scalability and performance.

🚧 Challenges we ran into

  • Fine-tuning the AI model to reduce false positives
  • Handling real-time updates in low-connectivity zones
  • Synchronizing data between emergency modules and map views
  • Designing a user-friendly interface suitable for emergency situations
  • Ensuring secure data handling and fast notification delivery

🏆 Accomplishments that we're proud of

  • Successfully built and deployed a full-stack accident response platform
  • Developed a working first-aid chatbot with over 500 predefined emergency instructions
  • Integrated ambulance routing with traffic-aware dispatch
  • Created a professional UI/UX focused on user stress and accessibility
  • Presented the project in mid-review with full documentation and testing

📚 What we learned

  • Working with real-time systems and geospatial APIs
  • Building scalable AI modules and integrating them with live apps
  • Handling real-world use cases with limited infrastructure
  • Designing software under user stress and critical time constraints
  • Collaborating effectively across frontend, backend, and AI teams

🔮 What's next for RoadSafe

  • Integrate drone-based medical kit delivery to remote accident locations
  • Incorporate voice assistant-based first aid guidance
  • Partner with hospitals and government emergency systems
  • Add predictive analytics to identify accident-prone zones
  • Scale to multi-language support and offline-first modes

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