The Challenge

When Richard Childress Racing (RCR) approached us, their pit crew faced two critical problems:

• Fuel Inefficiency: Overfilling wasted time; underfilling risked race-ending shortages
• Manual Analysis: Engineers spent hours reviewing footage to identify micro-delays

My mission: Build an AI system that could analyze pit stops in real time and prescribe optimal fuel quantities.

Building the AI Team & Approach

1. Assembled the Right Experts

• Computer Vision Leads: Recruited from sports analytics startups
• Time-Series Specialists: Hired engineers with F1/pit crew experience
• NASCAR Veterans: Embedded former crew chiefs to ground-truth our models

Key AI Innovations I Drove

1. Frame-by-Frame Pit Crew Kinetics

• Problem: Traditional video analysis missed sub-second delays
• Solution:
  • Trained a 3D CNN model on 12,000 historical pit stops
  • Detected micro-inefficiencies (e.g., 0.3s slower fuel-can handoffs)
  • Result: Identified 17% faster crew positioning sequences

2. Dynamic Fuel Calculation Engine

• Breakthrough:
  • Combined real-time telemetry (lap times, tire wear) with race simulation AI
  • Generated probabilistic fuel needs (e.g., “78% chance 12.4 gallons needed if caution in Lap 112”)
• Deployment:
  • Output to crew chiefs via augmented reality glasses during stops
  • Impact: Reduced fuel miscalculations from 9% to 2%

3. Failure Case Simulation

• Proactive Training:
  • Used GANs to generate synthetic pit disasters (spills, jack failures)
  • Trained crews via VR simulations
  • Result: 41% faster recovery from real incidents

Leading Through Adversity

1. Data Scarcity Early On

• Action: Partnered with NASCAR to access multi-team historical footage
• Innovation: Developed self-supervised learning to maximize unlabeled data

2. Crew Resistance to AI

• Solution:
  • Co-designed the interface with veteran crew members
  • Implemented voice-controlled AI alerts (“Watch left-rear tire changer-0.5s behind ideal”)
  • Outcome: 100% adoption by Season’s end

3. Real-Time Latency Crisis

• Breakthrough:
  • Edge-processed video on NVIDIA Jetson AGX in pit boxes
  • Achieved 97ms inference time (vs. 1.8s cloud alternative)

Results We Delivered

• 0.5s Faster Average Pit Stops (Key to 3 podium finishes)
• 98% Fuel Accuracy (Saved 2.1 gallons/race)
• 15% Fewer Penalties from improved crew coordination