Race_Mind_3D

RACEMIND 3D uses ARIE-V2 to predict laps, simulate scenarios, and forecast rival behavior, delivering real-time strategy and 3D telemetry insights for a decisive racing advantage.

Comment

InspiratiAbout the Project

RACEMIND 3D is a predictive race-intelligence system designed for the Toyota GR Cup. It shifts motorsport analytics from descriptive to predictive, using ARIE-V2 to forecast pace, understand driver behavior, detect fatigue, and provide real-time strategic recommendations. The goal was to build an intelligent engineering tool that answers the one question traditional dashboards cannot: “What will happen next?”

What Inspired the Project

Spec-series racing depends entirely on driver skill, not car performance. This makes small insights—fatigue, micro-errors, pace variations—extremely important.

Real race engineering fascinated me, especially how elite teams use forecasting and simulation.

I wanted to bring F1-level intelligence to a feeder series like the GR Cup, using telemetry and AI.

The idea of a virtual strategist capable of explaining decisions in real time inspired ARIE-V2.

What I Learned

How to process high-frequency telemetry and convert it into meaningful signals.

Designing a clean data pipeline for prediction and simulation.

Time-series forecasting, scenario modeling, and anomaly detection. Mathematical modeling like tyre-degradation behavior:

Δ 𝑡

deg

𝑘 ⋅ ln ⁡ ( 1 + lap ) Δt deg ​

=k⋅ln(1+lap)

Building multi-module AI systems (ARIE-V2).

Using Google AI Studio (Gemini) for real-time strategic reasoning.

Creating race-engineer style responses with contextual constraints.

Developing fast, readable dashboards for race-pressure environments.

How I Built the Project

Cleaned and validated telemetry data to ensure GR Cup compliance (no DRS, no tyre compounds, no unsupported metrics).

Built ARIE-V2, which includes:

Lap forecasting

Scenario simulation

Driver behavior mapping

Fatigue detection

Opponent threat analysis

Track-state learning

Implemented real-time visualization with React, TypeScript, and Three.js.

Designed sector delta tools, telemetry overlays, and behavior scoring.

Integrated Google AI Studio for the AI Race Engineer module.

Built a complete decision-support system with strategic recommendations.

Created a fully deployed app inside Google AI Studio for judges to test.

Challenges I Faced

Ensuring every prediction was dataset-driven—no assumptions allowed.

Removing all F1-specific logic and rebuilding GR-spec logic from scratch.

Extracting fatigue patterns using only telemetry (no biometric sensors).

Achieving stable predictions with limited historical samples.

Maintaining performance while running multi-scenario simulations.

Building a UI that remains readable, structured, and usable during real-time race pressure.

Making the AI Race Engineer produce strictly accurate, non-hallucinated, context-aware explanations.on

Share this project:

Updates