Capture bundle for the steering verification study

README.md

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+---
+license: apache-2.0
+task_categories:
+  - image-feature-extraction
+tags:
+  - autonomous-driving
+  - formal-verification
+  - neural-network-verification
+  - carla
+  - robustness
+size_categories:
+  - n<1K
+---
+
+# Steering verification captures
+
+Rendered camera frames along two full driving routes, in four weather conditions each.
+These are the input to the formal certificates in
+[AD-Assurance-Lab/formal-verification--steering--code](https://github.com/AD-Assurance-Lab/formal-verification--steering--code),
+and they are published because they are what makes those certificates checkable **without
+a simulator**. Certification reads captured frames, not a live CARLA server, so with this
+dataset and the code repository anyone can recompute every reported bound on one GPU — or
+on a CPU, more slowly.
+
+## What is here
+
+| | route | conditions | captured poses | frame size |
+|---|---|---|---|---|
+| `captures/town06/` | Town06 arterial, one 2,289 m lap (2,119 m scored) | clear, fog, night, low sun | 1,060 | 168×56 |
+| `captures/town04_v2/` | Town04 highway, both directions, 2,988 m spanned and 2,861 m scored | clear, fog, night, low sun | 1,492 per direction | 84×28 |
+
+The certifier bounds over every eighth captured pose — 133 per Town06 cell — which is the
+stride recorded in the committed certificate.
+
+Each `.npz` holds the frames for one condition along one route, together with the pose
+track they were captured at. `capture_gate.json` records the coverage the capture actually
+achieved, and `scope.json` the extent the study scores. 641 MB in total.
+
+The conditions are physically parameterized rather than pixel perturbations: fog density,
+sun altitude and the night preset are simulator parameters with physical meaning, and the
+certificate is quantified over the family that interpolates between two rendered
+endpoints. A norm ball in pixel space large enough to contain a night image also contains
+physically impossible images, and would make the safety claim vacuous.
+
+## Using it
+
+From a clone of the code repository, one command places every file where the certifier
+looks and verifies each one against a recorded digest:
+
+```bash
+python3 scripts/fetch_captures.py
+```
+
+Then, with no simulator running:
+
+```bash
+STUDY_MAP=Town06 python3 scripts/certify_town06.py --out /tmp/cert.json
+STUDY_MAP=Town04 TOWN04_REDO=1 python3 scripts/certify_sustained_bound.py
+```
+
+`SHA256SUMS` lists the digest of every file, and the same digests are recorded in
+`scripts/fetch_captures.py`. **Check them.** A capture is the certifier's entire input, so
+a bound computed from the wrong frames is not a weaker result — it is a statement about a
+different experiment, and it still prints a verdict and a margin and looks finished.
+
+## What reproduces, and how exactly
+
+Every verdict reproduces. The bounds reproduce to about 4 parts in 1,000, not exactly.
+Bound propagation with branch-and-bound makes different splitting choices when tiny
+floating-point differences reorder them, and that grows with network size; the same binary
+run twice on the same GPU already differs by 2.5 parts in a million. Every verdict in the
+study has at least 309 times more headroom than that, so the drift cannot change a
+conclusion — but a changed *verdict*, or a bound that moves by more than a percent, means
+something real is different and is worth investigating rather than accepting.
+
+## What is not here
+
+The training frames, about 59 GB, are not published. They are regenerable from the code
+repository, nothing in the paper is checked against them directly, and they were collected
+under a specific simulator configuration that makes them non-reusable across harness
+changes.
+
+## Citation
+
+```bibtex
+@software{ad_assurance_lab_steering_verification,
+  author  = {{AD Assurance Lab, Western Michigan University}},
+  title   = {Formal verification of end-to-end steering under
+             physically parameterized weather},
+  year    = {2026},
+  doi     = {10.5281/zenodo.22101297},
+  url     = {https://github.com/AD-Assurance-Lab/formal-verification--steering--code}
+}
+```
+
+Apache 2.0, the same as the code.

SHA256SUMS

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+591b909c5a5605ffa76e33fe897b451928c5aead0fc4255af5b8dece870bfe3b  captures/town06/capture_gate.json
+cd583d61cb9f8c812d48106ed0c1e8c552476f4fd09d91e8c8f7b3043c174f21  captures/town06/lap_lap_clear.npz
+f6cbb3fd3aa3168b796460bda62e4f68893185192416e9bfe1d297f4443cab1e  captures/town06/lap_lap_fog.npz
+9107aecf74cf6fc96ce6276e5782071b6b83e14970ad8e327c3cf8ab83b76fc4  captures/town06/lap_lap_low_sun.npz
+abe8f269cbd03f4853445f005b008ee20c82096abf3aeaf53738c523e1723c6b  captures/town06/lap_lap_night.npz
+85f0a7ceae809837615b57dc4c242f895c97247b39e3ad0d9158bdbcef24232c  captures/town04_v2/capture_gate.json
+22cf8d35d04eaa43b12ae4251bf18e5a1b67b91c23e00a34a3adcf1376f90cd1  captures/town04_v2/scope.json
+9e3a19fd60e03ff58530779134716e23506c55956591b12ff5a5bf0c4a9fcb96  captures/town04_v2/lap_eastbound_clear.npz
+23f56df0565a787f0ccc3b13faf66ca43105659eef9fd120d7825c4c24a7f573  captures/town04_v2/lap_eastbound_fog.npz
+2a4ada73c230d13be0495f9713c889cefecaeb0b997d49d74c048baa6ea078a8  captures/town04_v2/lap_eastbound_night.npz
+b58d68c51f565c2280a0128e0e5a946b70c528cb18180e3cfb48aa364d777427  captures/town04_v2/lap_eastbound_shadows.npz
+368dc433a70a29e53a51690cbf26bb07e14bb770514fd585d306114db216f098  captures/town04_v2/lap_westbound_clear.npz
+c07b5caa4e21f9efb48fa4ef8c695be01c4120fee4f7b326b00dcec30d94d78e  captures/town04_v2/lap_westbound_fog.npz
+46d02215d96bd2b4846b94ad80c17bfbaf82ab924811a7387bcae611998ee3fa  captures/town04_v2/lap_westbound_night.npz
+3f45779f32b0ba92b7d8d0b9cc6da59b7dc7f584ca8a508ddfa32d290cb9623f  captures/town04_v2/lap_westbound_shadows.npz

captures/town04_v2/capture_gate.json

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+{
+  "threshold": 0.05,
+  "worst": 0.006637363535212384,
+  "cells": [
+    {
+      "student": "S_clear",
+      "checkpoint": "S_clear_84x28_v2",
+      "direction": "eastbound",
+      "cond": "clear",
+      "mean_abs_diff": 0.006585094666857052,
+      "poses": 1488,
+      "passed": true
+    },
+    {
+      "student": "S_clear",
+      "checkpoint": "S_clear_84x28_v2",
+      "direction": "westbound",
+      "cond": "clear",
+      "mean_abs_diff": 0.006637363535212384,
+      "poses": 1487,
+      "passed": true
+    },
+    {
+      "student": "S_mixed",
+      "checkpoint": "S_mixed_84x28_w3_v2_dagger_r00",
+      "direction": "eastbound",
+      "cond": "clear",
+      "mean_abs_diff": 0.005442611279963736,
+      "poses": 1488,
+      "passed": true
+    },
+    {
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+      "checkpoint": "S_mixed_84x28_w3_v2_dagger_r00",
+      "direction": "westbound",
+      "cond": "clear",
+      "mean_abs_diff": 0.0050745457005276825,
+      "poses": 1487,
+      "passed": true
+    }
+  ],
+  "cells_expected": 4
+}

captures/town04_v2/lap_eastbound_clear.npz

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captures/town04_v2/scope.json

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+{
+  "certificate": "results/town04_v2/calibration/sustained_bound.json",
+  "derived_by": "scripts/attest_certificate_scope.py",
+  "note": "Derived from the committed captures, not recorded by the certifier. This certificate predates lap_end_m in _meta and is the pre-registered artifact, so it is not regenerated.",
+  "lap_end_m": 2988.0,
+  "capture_span_m": {
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+    "lap_eastbound_fog.npz": 2988.0,
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+    "lap_westbound_night.npz": 2987.9,
+    "lap_westbound_shadows.npz": 2987.9
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+  "consistent": true
+}

captures/town06/capture_gate.json

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+  "cells": [
+    {
+      "student": "S_clear_t06",
+      "checkpoint": "S_clear_t06lap_168x56_w2_s0",
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+      "cond": "clear",
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+      "poses": 1055,
+      "passed": true
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+      "passed": true
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