LapLens

LapLens turns raw TRD GR86 telemetry and lap times into a driver-ready race debrief – scoring every lap 0–100 and generating instant coaching notes to show exactly where time was left on track.

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Inspiration

After a race, drivers and engineers only get a short window to answer hard questions:
Where did we leave time on the table? Which laps should we actually review?

The TRD datasets are incredibly rich, but they live in multiple CSVs and raw telemetry channels. I wanted a way to compress that complexity into something a driver can understand in minutes: one screen, one score, and concrete coaching notes.

What it does

LapLens turns TRD GR86 telemetry + lap-time data into a driver-focused post-event debrief:

  • Rebuilds each lap from telemetry, lap start/end, and official lap times.
  • Computes lap-level metrics: average speed, throttle usage, brake pressure, and sample quality.
  • Derives a LapLens score (0–100) to rank laps by quality.
  • Generates short, per-lap coaching notes that describe whether a lap is conservative, messy, or a strong benchmark.
  • Visualizes performance trends and how LapLens score relates to official lap time.

How we built it

  • Started in a Jupyter notebook to explore the COTA Race 1 GR86 data and validate assumptions.
  • Wrote a preprocessing pipeline in Python (src/preprocess.py) to:
    • Load TRD telemetry, lap start/end, and lap time files.
    • Align ECU timestamps with lap windows.
    • Assign telemetry rows to laps and build lap aggregates.
  • Designed a simple LapLens scoring formula from normalized speed, throttle, braking, and lap time.
  • Wrapped everything in a Streamlit web app so judges and engineers can interactively:
    • Select a car and outing.
    • See KPIs, per-lap table, trends, and coaching notes in one place.

Challenges we ran into

  • Time alignment: The ECU clock and lap-timing timestamps did not perfectly match. I had to build an alignment step that computes an offset and then re-assigns telemetry to laps.
  • Noisy laps: Some laps had unrealistic lap numbers or very few samples. I added quality filters (e.g., minimum sample count) so LapLens only scores meaningful laps.
  • Balancing simplicity vs detail: It was tempting to add many features, but I focused on a small set of metrics (speed, throttle, brake, lap time) that map clearly to how drivers talk about pace and consistency.
  • Stability: Working with large CSVs in a notebook and in Codespaces meant being careful with memory and avoiding overly heavy computations.

Accomplishments that we're proud of

  • Built an end-to-end pipeline: from raw TRD CSVs to a working, testable web app.
  • Created a LapLens score that correlates with lap time but still reflects driving style, not just raw pace.
  • Turned low-level telemetry (G-forces, throttle percentages, brake bar) into natural-language coaching notes that a driver can actually act on.
  • Delivered a UI where an engineer can understand a stint in seconds: best lap, worst lap, consistency, and which laps to review first.

What we learned

  • How to reconstruct laps robustly from telemetry, lap start/end, and timing data when clocks don’t perfectly align.
  • How to design metrics that are interpretable, not just mathematically clever – the 0–100 LapLens score and the Driver Consistency Index both need to “feel right” to a human.
  • The importance of focusing on driver language (“push lap”, “conservative”, “over-driving”) rather than only technical terms.
  • How to turn a notebook exploration into a clean, cached Streamlit app that judges can run easily.

What's next for LapLens

  • Extend beyond a single GR86 stint to compare multiple cars and races (e.g., COTA vs Sebring vs VIR).
  • Add corner-level and sector-level overlays once track maps and distance channels are integrated.
  • Allow exporting debrief reports for drivers (PDF/HTML) and integrating LapLens scores into team tools or simulators.
  • Experiment with more advanced models to suggest “ideal” target laps or predict how much time is available with cleaner driving.

Built With

  • and-the-official-trd-gr86-csv-datasets-(telemetry
  • git/github
  • github
  • github-codespaces
  • jupyter-notebook
  • lap-start/end
  • matplotlib
  • numpy
  • pandas
  • python
  • seaborn
  • streamlit
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