RaceIQ: AI-Powered Telemetry Analysis

RaceIQ democratizes race engineering by combining professional-grade telemetry visualization with an AI Race Engineer that provides instant, natural language insights.

🚀 Features

• Live Track Map: Real-time vehicle position with sector-based motion trails.
• AI Race Engineer: Ask questions like "How can I improve my sector 2 time?" and get instant analysis.
• Driver Comparison: Head-to-head delta heatmaps.
• Race Strategy: AI-driven pit stop and tire management recommendations.

🛠️ Installation

Prerequisites

• Node.js (v18+)
• Python (v3.10+)

1. Clone the Repository

git clone https://github.com/DavidKasompe/IQ.git
cd IQ

2. Backend Setup

cd backend
python -m venv venv
# Windows
venv\Scripts\activate
# Mac/Linux
source venv/bin/activate

pip install -r requirements.txt

3. Frontend Setup

cd ../frontend
npm install

📂 Data Setup (Important!)

Due to file size limits, the raw telemetry data is not included in this repository.

1. Download the Dataset: (Provide link to Google Drive/Dropbox here if available, or state "Provided separately for judging")
2. Place the Data:
   • Create a folder backend/data (if it doesn't exist).
   • Place the barber-motorsports-park folder inside backend/data (or update backend/app/config/tracks.json to point to your data location).

Note for Judges: The application is configured to look for data in backend/data by default. Please ensure the telemetry CSV files are present for the "Live Telemetry" feature to work.

🏃‍♂️ Running the App

1. Start Backend:

   cd backend
   python -m uvicorn app.main:app --reload

2. Start Frontend:

   cd frontend
   npm run dev

3. Open http://localhost:5173 in your browser.

🎥 Demo Video

(Link to your YouTube/Vimeo demo video)