Pit Commander - AI Race Engineer Assistant

Category: Real-Time Analytics

The Problem In professional motorsports, races are won and lost in split-second decisions. When should a driver pit? Are the tires about to fall off a performance cliff? Will an undercut work? Race engineers juggle dozens of variables simultaneously while under immense pressure. Traditional tools—stopwatches, radio communication, and gut instinct—aren't enough.

My Solution Pit Commander is an AI-powered race strategy assistant that analyzes real-time telemetry data to provide actionable recommendations to race engineers. Using machine learning models trained on historical Toyota GR Cup race data from Indianapolis Motor Speedway, my system predicts tire degradation, optimizes pit stop timing, and forecasts race outcomes—all in under 500 milliseconds.

Key Features

Real-Time Dashboard: Simulates live race conditions with streaming telemetry (speed, throttle, brake pressure, tire age, fuel levels)
AI Strategy Advisor: Machine learning models recommend optimal pit windows with confidence scores and reasoning
Caution Flag Optimizer: Instant decision support when yellow flags drop—pit now or stay out?
What-If Simulator: Rewind to any lap and test alternative strategies to see "what could have been"
Driver Performance Analysis: Sector-by-sector breakdown showing where drivers gain or lose time

Technical Implementation Backend: FastAPI (Python) with scikit-learn ML models (Random Forest for tire degradation, custom logic for pit strategy) Frontend: React + TypeScript with TailwindCSS and Recharts for data visualization Data Pipeline: Processed 500,000+ telemetry data points from Indianapolis dataset Deployment: Vercel (frontend) + Railway (backend) for production-ready access

Impact Pit Commander transforms hours of post-race analysis into real-time insights. By reducing decision-making latency from minutes to milliseconds, teams can execute better strategies and drivers can focus on what they do best—racing. Beyond GR Cup, this system could scale to other racing series, democratizing access to professional-grade analytics tools.

What I Learned

Racing data is messy—GPS drift, missing timestamps, and sensor noise required sophisticated cleaning
Real-time UX design is critical—users need information density without overwhelming clutter
ML model interpretability matters—race engineers trust recommendations more when they understand the "why"

What's Next

Multi-track support (Road America, COTA, Sebring)
Predictive weather integration for rain strategy
Team communication features for multi-car coordination

Built For: Hack the Track presented by Toyota GR Datasets Used: Indianapolis Motor Speedway telemetry, lap times, and sector data Tech Stack: React, TypeScript, TailwindCSS, FastAPI, Python, scikit-learn, Pandas, Recharts