Sepang Vision Lab

Sepang Vision Lab puts an interactive digital twin of Sepang next to the tools a strategist would want: historical replay, telemetry, lap-time models, pit-strategy and Monte Carlo simulation, weather scenarios, webcam hand tracking and an AI race engineer.

It is deliberately not a driving game, a fantasy manager or a chatbot with racing trivia. The question it explores is how far honest, public race data can go before you have to start guessing, and how to show clearly where that line is.

Race strategy is made from partial information: lap times, gaps, pit windows, weather that might arrive. Public data covers only a slice of it. There is timing, but no pedal telemetry, no tyre compounds and no car positions between timing lines.

Most visualisations fill those gaps quietly with plausible-looking numbers. I wanted a system that used what the data actually supports, modelled what it could, and labelled everything else as an assumption.

Each capability depends on the one before it, so the project was built strictly in that order. No layer started until the one beneath it worked and was tested.

Sepang Vision Lab is under construction. These are captures of the current local build on the 2017 Malaysian Grand Prix timing archive. Movement is interpolated between timing lines, not GPS, and the screens say so.

The lap-time experiment predicts lap N from the three previous laps, the lap number and the last recorded position. Splits are chronological across the whole field (train by 45 min, validate to 65 min, test after), so no model ever sees the future.

Random Forest won on validation MAE and was selected. On the test window it did not beat the naive previous-lap baseline, and the app says so prominently instead of hiding it.

Still open: the gesture model is waiting on real labelled recordings, and the live AI race-engineer path still needs end-to-end verification. Both are marked as open in the project rather than shown as finished.