# Chart contract — semantic work-zone digital-twin pilot

Surface: executed notebook, static Extended Abstract figures, and one 16:9
research-card image. Renderer: Matplotlib from saved CSV/NPZ evidence.

## Figure 1 — reconstruction and operational semantics

- **Question:** Does one robot patrol produce a metric 3-D representation that
  can be lowered into operationally distinct zones?
- **Takeaway sought:** Show the evidence chain without implying field autonomy:
  reference scene → fused semantic point cloud → traversable / inspect / no-go /
  unknown bird's-eye map.
- **Form:** three-panel 3-D scatter + 3-D scatter + categorical raster. This is a
  method overview, not a performance chart.
- **Data:** one preregistered representative layout (`right_lane_closure`), fixed
  seed 20260808, `loop_patrol` strategy.
- **Palette:** road neutral grey, TTC device orange, barrier navy, work vehicle
  gold; operational traversable teal, inspect orange, no-go navy, unknown pale
  grey. Markers and direct labels supplement color.
- **Output:** `semantic_reconstruction_overview.png` and `.svg` at 16:9; the
  PNG also serves as the Research card image after final-page QA.

## Figure 2 — patrol strategy comparison

- **Question:** How do a single pose, one-way patrol, and out-and-back loop
  change reconstruction completeness and operational-map quality?
- **Takeaway sought:** More viewpoints should improve completeness and actionable
  mIoU; the live-lane false-traversable rate must be shown separately because it
  is a failure metric.
- **Form:** three aligned grouped-bar panels with mean and 95% t-interval across
  15 runs per strategy (3 layouts × 5 seeds). Absolute axes begin at zero.
- **Fallback:** if a metric has nearly no between-run variation, retain exact
  direct labels and show the interval rather than inventing a trend.
- **Palette:** single teal root for quality measures; orange for the signed
  failure measure. Strategy identity uses tone and hatch, not color alone.
- **Output:** `patrol_strategy_comparison.png` and `.svg`, plus aggregate CSV.
  The FTR panel must print each strategy's known coverage and state that a zero
  FTR under low coverage is not evidence of greater safety.

## Figure 3 — layout robustness matrix

- **Question:** Does operational-map IoU hold across different illustrative
  work-zone boundary shapes?
- **Takeaway sought:** Reveal layout-specific weaknesses instead of relying on an
  overall average.
- **Form:** heatmap of mean actionable mIoU by layout and patrol strategy, five
  fixed seeds per cell, with values printed in every cell.
- **Palette:** single-root sequential teal; fixed 0–1 scale.
- **Output:** `layout_robustness_matrix.png` and `.svg`.

## Figure 4 — failure-aware map comparison

- **Question:** What information is missing when the robot observes the scene
  from one pose, and what becomes mapped after a loop patrol?
- **Takeaway sought:** Unknown/abstain is preferable to silently labeling an
  unobserved live lane as traversable.
- **Form:** paired categorical BEV maps for the same layout and seed; identical
  axes and ontology.
- **Palette:** same operational ontology as Figure 1; unknown is visibly neutral.
- **Output:** `failure_aware_maps.png` and `.svg`.

## QA requirements

- Every figure states `deterministic procedural sensor-fusion pilot` in its
  caption or footer; none may be labeled as CARLA, real LiDAR, or hardware data.
- `actionable mIoU` is the unweighted mean IoU of traversable, inspection, and
  no-go cells over the full labeled ROI; unknown predictions count as misses.
- `false-traversable rate` uses ground-truth no-go cells as the denominator.
- Confidence intervals summarize the 15 fixed layout-seed runs and are not
  population-level safety uncertainty.
- Inspect PNGs at their final webpage width; no clipped labels, unreadable
  legends, or color-only distinctions are acceptable.
