# LAB E11 — Full-session integrated perception stability qualification

Date: 2026-07-22
Accepted result creation time: 2026-07-22T06:27:51.941Z
Laboratory status: **accepted**
Navigation or safety status: **not accepted**

## Objective

LAB E11 tested whether the integrated Mission Core perception contour proven on
the 60-second LAB E10 interval remains bounded and source-paced across the
complete recorded right-camera epoch from `RAVNOVES00`.

The qualification retained the intended onboard architecture:

```text
recorded camera at original source timestamps
  -> bounded latest-wins detector queue
  -> YOLOX-S + ByteTrack-compatible tracking at camera rate
  -> bounded sampled semantic queue
  -> EoMT Cityscapes semantics at 1/5 camera rate
  -> factory camera_1 KB4 camera/LiDAR projection
  -> visible LiDAR-supported 3D cuboids and distance
  -> clearance sectors and timestamped world state
```

This was not an offline maximum-quality render. The replay speed was exactly
`1.0`, queues were bounded, and acceptance required the worker to keep up with
the recorded source without detector loss.

## Result summary

Accepted immutable result:

`e10-integrated-perception-7613b09021455ddec941144224142f7879e3c6ecac5b2370ea8a7d4cb01918ba`

Worker result root:

`D:\NDC_MISSIONCORE\runtime\derived\e10-integrated-perception-7613b09021455ddec941144224142f7879e3c6ecac5b2370ea8a7d4cb01918ba`

Local verified mirror:

`.runtime/compute-experiments/e10/worker-results/e10-integrated-perception-7613b09021455ddec941144224142f7879e3c6ecac5b2370ea8a7d4cb01918ba`

| Measure | Result | Frozen gate | State |
|---|---:|---:|---|
| Source frames | 4,489/4,489 | exactly 4,489 | pass |
| Detector drops | 0 | 0 | pass |
| Detector effective rate | 10.004402 FPS | >=9.5 FPS | pass |
| Semantic results | 898/898 scheduled | accounting exact | pass |
| Semantic drops | 0 | <=5% | pass |
| Semantic effective rate | 2.001326 FPS | >=1.8 FPS | pass |
| Fresh semantic coverage | 99.754956% | >=90% | pass |
| Semantic completion age p95 | 267.211864 ms | <=400 ms | pass |
| World-state age p95 | 102.714712 ms | <=175 ms | pass |
| LiDAR-fused frames | 3,917 | >=3,500 | pass |
| Accepted cuboid observations | 6,157 | >=500 | pass |
| Inference/producer failures | 0 | 0 | pass |
| Fusion with non-fresh semantics | 0 | 0 | pass |

The complete 448.623-second camera interval was replayed in 448.702 seconds.
This is source-paced operation, not an accelerated batch claim.

## Frozen inputs

### Recorded source

- UI session alias: `RAVNOVES00`;
- session ID: `20260720T065719Z_viewer_live`;
- compute job: `recorded-camera-602ac89026ed12978619801d`;
- job input SHA-256:
  `602ac89026ed12978619801d4edea0cae24b5cc3afabd9f7af2858de6505a20e`;
- source: `sensor.camera.right`;
- codec epoch: `1`;
- resolution: 800x600;
- source frame indices: 0–4,488 inclusive;
- camera timeline: 35.421857292–484.044857292 seconds;
- timeline SHA-256:
  `f91dad0a3cc998f4250be794b48f437943b3c60edccbe7e5ceed59030854a807`;
- job payload: 363,235,615 bytes;
- raw MQTT evidence: 133,572,722 bytes.

### Factory calibration

- slot: `camera_1`;
- content identity SHA-256:
  `05f3ad9b38b3a4fc95388a8ec83da83c745e217709e51787b3d5aad0969f6fa9`;
- projection: factory KB4 intrinsics plus camera/LiDAR extrinsic;
- LiDAR coordinate source: K1 map frame.

No calibration was fitted or modified during LAB E11.

### Full LiDAR replay pack

Immutable pack:

`e10-lidar-pack-576c994a6c814e2592dd6240ace3902a5db94843312c759a73ba0c9166157d2b`

- frames: 4,489;
- frames with accepted LiDAR and pose: 3,928;
- strict-sync coverage: 87.502785%;
- point observations: 9,207,270;
- compressed payload: 72,996,000 bytes;
- payload SHA-256:
  `0685d24219d8236caf8b7f1685e93f6d6b59e7fd015a768d88a92bbe8b154944`;
- builder SHA-256:
  `bc81a1aa8759a750480745a52af34f4c700261e8bfbf5d615e8c6e5fa96bb69c`;
- accepted E6 temporal-profile SHA-256:
  `b8d3a6f043fde54ffd32ea05bfe580e2be7aace1a23cdbf7e266eb3325d147b2`;
- accepted E6 result:
  `e6-fusion-b4e4226674a66f6196c033785eb307c255a7bdd8493ef9809dfb1d6e5bd68eaa`.

The pack uses the unchanged E6 temporal policy:

- clock source: recorded host monotonic arrival;
- binding: nearest host-arrival best effort;
- maximum camera/LiDAR delta: 100 ms;
- maximum point/pose delta: 100 ms.

The pack is a replay optimization and immutable input. It does not improve or
reinterpret synchronization accuracy.

## Frozen software configuration

### Integrated scheduling profile

Profile: `e10_full_session_profile.json`
SHA-256: `73dd8472b59c9ad48b781600c89eadb0aab12cb6ef59c4572ce5a8e17dcd4472`

- mode: `full-session-qualification`;
- replay speed: 1.0;
- detector queue: latest-wins, capacity 2;
- semantic queue: latest-wins, capacity 1;
- semantic scheduling: every fifth camera frame;
- semantic TTL: 750 ms;
- detector loss gate: exactly zero;
- required source selection: frames 0–4,488 and at least 448 seconds.

### Producer identities

| Component | SHA-256 |
|---|---|
| Integrated runner | `5548cc607f245cd1c01c85d6b821fdf5b91e512ee9b2f8ad6862175c93b7628c` |
| Fusion runtime | `844a3a3ecc56313be2f96a6b861abd8f55100ec7427a72379e52a55fbd4c5749` |
| PowerShell orchestrator | `ac0ee42495839773a2de60090ebcb49eee979dc7ee3b8587662a3c59e19a4d03` |
| Detector profile | `819bdfb0da521d187ec9ce3d3039a0ff75daa0ab473fb3d8e5fa92db9c1ddca1` |
| Semantic profile | `ea583966bc3409f5cf563cbf4fad05e366907e67187082eb692aff53d9f5d875` |

Worker runner generation:

`D:\NDC_MISSIONCORE\runtime\derived\e10-runner-5548cc607f24-ac0ee4249583`

### Models

Detector:

- YOLOX-S, COCO-80;
- ONNX SHA-256:
  `c5c2d13e59ae883e6af3b45daea64af4833a4951c92d116ec270d9ddbe998063`;
- Triton config SHA-256:
  `5795c737a7935a655961b069e8404d336d891f9762fb6dffb93956a076479604`.

Semantic model:

- `tue-mps/cityscapes_semantic_eomt_large_1024`;
- revision `8d6b6d1a3f7b50d441afd7d247c2ed10db186e8f`;
- `model.safetensors` SHA-256:
  `c265da9a74f58f5c3f4826d23ca4ca78beac0b106cca5842beca61580de5b782`;
- precision: FP16 autocast;
- batch size: 1.

Valid-FOV mask:

- generation:
  `valid-fov-mask-b4dd8ddf2b87c1d520ee8a0868c4fea062d7c14d1bae73ccabd3abe1f3acbac2`;
- mask SHA-256:
  `a40cee06b7c6f69b6a09a11563dcfd237f3de833b1ccd31459e66692e528ba63`;
- admitted pixels: 270,606/480,000.

## Worker and resource envelope

- GPU: NVIDIA GeForce RTX 4090;
- container: `nvcr.io/nvidia/tritonserver:26.06-py3`;
- Python: 3.12.3;
- PyTorch: 2.13.0+cu130;
- Transformers: 4.57.6;
- NumPy: 1.26.4;
- SciPy: 1.16.3.

GPU telemetry, 449 one-second samples:

| Measure | Mean | p95 | Max |
|---|---:|---:|---:|
| GPU utilization | 67.70% | 92% | 96% |
| Board GPU memory used | 13,151.48 MiB | 13,161 MiB | 13,161 MiB |
| GPU-memory controller utilization | 16.40% | 33% | 34% |
| Power | 207.34 W | 215.51 W | 229.74 W |
| Temperature | 48.46 C | 53 C | 54 C |

Process and CUDA measurements:

- process peak RSS: 2,944.93 MiB;
- CUDA peak allocated by the runner: 2,107.93 MiB;
- CUDA peak reserved by the runner: 2,840 MiB.

The board-memory figure includes the existing Triton/model context and is not
equivalent to runner-only allocation.

## Latency detail

| Stage | Mean | p95 | Max |
|---|---:|---:|---:|
| Frame decode | 34.198 ms | 47.488 ms | 84.767 ms |
| Detector | 32.099 ms | 47.759 ms | 70.328 ms |
| Detector queue wait | 0.230 ms | 0.202 ms | 33.917 ms |
| LiDAR projection | 0.491 ms | 0.940 ms | 7.396 ms |
| Object association | 2.176 ms | 5.795 ms | 20.093 ms |
| Clearance | 0.236 ms | 0.577 ms | 4.740 ms |
| World-state construction | 0.117 ms | 0.333 ms | 4.562 ms |
| End-to-end world-state age | 71.634 ms | 102.715 ms | 189.403 ms |

Semantic inference:

| Stage | Mean | p95 | Max |
|---|---:|---:|---:|
| Processing | 191.693 ms | 209.808 ms | 448.629 ms |
| Forward | 156.680 ms | 170.785 ms | 414.754 ms |
| Completion age | 228.427 ms | 267.212 ms | 477.220 ms |
| Semantic queue wait | 0.186 ms | 0.279 ms | 4.548 ms |

The acceptance contract uses p95 rather than the isolated maximum. Five startup
frames had no semantic state and six later frames observed a stale state; no 3D
fusion was asserted on any non-fresh semantic state.

## Fusion detail

- LiDAR available: 3,928 frames;
- fused with fresh semantics: 3,917 frames;
- fused share of LiDAR-available frames: 99.719959%;
- depth unavailable under the frozen sync gate: 561 frames;
- semantic unavailable: 5 frames;
- semantic stale: 6 frames;
- accepted cuboid observations: 6,157;
- fusion freshness violations: 0.

Rejections are expected diagnostic outcomes:

| Reason | Count |
|---|---:|
| Insufficient 5-point support | 8,268 |
| No semantic LiDAR support | 4,892 |
| Distance innovation gate | 1,045 |
| Insufficient 3-point support | 142 |
| Implausible cuboid extent | 24 |

These counts describe per-frame object observations, not unique physical
objects.

## Disk safety and storage behavior

The worker orchestrator was confined to `D:\NDC_MISSIONCORE`. Drive C was not
read or written by this task.

- configured D free-space floor: 360 GiB;
- preflight free space: 376.506 GiB;
- calculated additional reserve requirement: 11.834 GiB;
- after full stream reconstruction: 376.127 GiB;
- after 4,489 PNG extraction: 373.578 GiB;
- after publication, before temporary cleanup: 373.347 GiB;
- after cleanup and model-state restoration: 376.223 GiB.

Temporary frames and the reconstructed camera stream were deleted by the
orchestrator. No raw recording, prior result, model, cache, or accepted runner
generation was deleted.

Known orchestrator timings:

- frame preparation: 31.425 seconds;
- source-paced replay: 448.702 seconds;
- total orchestrator wall time, including model validation/loading and cleanup:
  600.623 seconds.

## Published artifacts

| Artifact | Bytes | SHA-256 |
|---|---:|---|
| `semantic-frames.jsonl` | 404,380 | `49b1a859924b26ab5f550ca3c6d6ec907c32c0ef01870c270c33ef3eb103db53` |
| `fusion-frames.jsonl` | 11,322,702 | `67ee78bc1385e5733ff33106fb7d257825ce297f69356af763c2598a531e8f3c` |
| `world-state.jsonl` | 11,703,966 | `d4772484dd0740ba009a947114d4c6bde7b5fc2ffbe55ddf7a341cf9aa11e5a5` |
| `transient-perception.npz` | 8,538,476 | `e53f0aea9b94704cd95c898b4b6f9c21da4fdad864373889213a9c1d66080ccf` |
| `gpu-telemetry.jsonl` | 100,714 | `2fd0d8f9497f5cff06a3035b9f251c780a0f4fcccf14d3a89f49a18584f43e92` |
| `run-report.json` | 14,076 | `3f0577b0dc8b55b38b8528c8f081be71af856327485f088f0ab0305e5a731ab1` |

Publication scope is
`recorded-integrated-realtime-qualification-only`. The accepted result is
eligible to become the newest recorded integrated overlay for this session.

## Verification

The copied result was independently validated on the Mission Core host using
`validate_integrated_perception_result`. Validation rechecked:

- compute-job identity;
- result and configuration identities;
- full contiguous source selection;
- LiDAR-pack identity and calibration binding;
- artifact sizes and SHA-256 values;
- 4,489 fusion rows and 4,489 world-state rows;
- semantic timeline ordering;
- NPZ array shapes and offsets;
- report/result acceptance consistency.

The focused automated suite completed with 24 passing tests, including the E10,
E8, E9, live-perception and qualification validators.

## Controlled preflight rejection

An initial invocation passed the base E5 detector profile where the integrated
runner requires the E8 real-time tracking wrapper. The schema guard rejected it
during preflight with `LAB E8 profile schema changed`.

That invocation did not extract frames, start replay, publish a result, or alter
model state. The accepted invocation changed only the detector-profile argument
to the already frozen E8 profile. This rejected preflight is retained here so
the laboratory history is complete.

## What LAB E11 proves

LAB E11 proves that the current RTX 4090 worker and bounded scheduling contour
can replay the complete recorded `RAVNOVES00` right-camera epoch at source rate
while producing:

- 10 Hz 2D detection/tracking;
- approximately 2 Hz semantic state;
- factory-calibrated LiDAR-supported distance and visible-surface cuboids;
- timestamped world state and clearance;
- bounded queues with no detector or semantic loss in this run.

It also shows that the one-minute LAB E10 result was not a short-interval
artifact: source-paced behavior remained stable for the full 448.623 seconds.

## What LAB E11 does not prove

- direct live K1 camera/MQTT ingestion into this scheduler;
- hardware-clock camera/LiDAR synchronization;
- forest-domain accuracy or robustness;
- safety or navigation fitness;
- complete amodal 3D object dimensions;
- multi-camera fusion;
- performance on weaker onboard compute hardware.

YOLOX COCO and EoMT Cityscapes are generic models. Cuboids describe the visible
LiDAR-supported surface selected by the current association policy, not a
ground-truth full object volume.

## Next gate

The next priority is not another offline quality-only pass. It is a transport
integration gate:

1. feed the same bounded scheduler from direct K1 camera and LiDAR/MQTT sources;
2. keep the latest-wins queues and freshness rules unchanged;
3. expose live world-state age, drop counters and semantic freshness;
4. qualify a controlled near-real-time live interval on this same worker;
5. only then compare lower-power onboard targets or more domain-specific models.

The full-session recorded overlay may be rendered on demand for visual QA, but
its generation is presentation work and is not required to accept the compute
stability result.
