Datasets:

AD-Assurance-Lab
/

aeb-verification-captures

---
license: apache-2.0
task_categories:
  - image-feature-extraction
tags:
  - autonomous-driving
  - formal-verification
  - carla
  - automatic-emergency-braking
size_categories:
  - n<1K
---
# Camera frames and trained networks for the braking verification study

The inputs to `AD-Assurance-Lab/formal-verification--aeb--code`, measured on 2026-09-10.

**Why this exists.** The study's results are in git. These files are not: they are large and
git ignores them. Without them the published numbers can be reproduced **in kind** but never
**exactly**, because the simulator does not render bit-identical frames from one run to the
next. Different frames give different networks, which give different certificates.

## What is here

| | |
|---|---|
| `captures/Town01/` | 66 frame sets, one per scenario and sun altitude, plus the campaign manifests |
| `models/Town01/` | 6 trained networks, three policies for each of two scenarios |
| `MANIFEST.json` | a hash of every file, and the capture harness recorded inside every frame set |

## How the pieces fit

A frame set is a rendered approach at one sun altitude: images, the range to the conflict
point at each pose, and a stamp saying what made it. The stamp records the map, the cloud
cover, the settle time, the determinism rules digest, the texture streaming and quality
settings, and the order the knots were captured in. That last field exists because capture
order silently ruined a dark endpoint once and nothing recorded it.

The networks are what the certificates are computed on. Each certificate in the code
repository names its network by hash, and those hashes are in `MANIFEST.json`. If they do
not match, the certificate and the network are not a pair and nothing may be concluded from
them together.

## Using them

Clone the code repository, put these directories at `results/captures/` and
`results/models/`, then:

```bash
python -m study.status                     # where the study stands
python tools/family_fidelity.py            # how much light each piece of the range spans
```

Both run without a simulator.

**Training reproduces exactly from these frames.** Four separate runs gave byte-identical
checkpoints. So the networks here can be regenerated from the frames here, and the
certificates from the networks. The chain is only broken if the frames are lost.

## What the study found

Three policies differing only in which light levels their training frames came from. All
three pass all three lighting conditions the standard tests, ten times out of ten, so the
standard's own procedure cannot tell them apart.

Driven at 65 light levels between those conditions, three repetitions each: the policy
trained on the whole range crashes 0 times, and the policy trained on the regulatory test
points crashes 21 times on the lead-vehicle scenario and 9 on the crossing pedestrian. A
certificate names those light levels without simulating them.

Method and results: `AD-Assurance-Lab/formal-verification--aeb--code`, `docs/STUDY_REPORT.md`.

	---
	license: apache-2.0
	task_categories:
	- image-feature-extraction
	tags:
	- autonomous-driving
	- formal-verification
	- carla
	- automatic-emergency-braking
	size_categories:
	- n<1K
	---

	# Camera frames and trained networks for the braking verification study

	The inputs to `AD-Assurance-Lab/formal-verification--aeb--code`, measured on 2026-09-10.

	Why this exists. The study's results are in git. These files are not: they are large and
	git ignores them. Without them the published numbers can be reproduced in kind but never
	exactly, because the simulator does not render bit-identical frames from one run to the
	next. Different frames give different networks, which give different certificates.

	## What is here

	\| \| \|
	\|---\|---\|
	\| `captures/Town01/` \| 66 frame sets, one per scenario and sun altitude, plus the campaign manifests \|
	\| `models/Town01/` \| 6 trained networks, three policies for each of two scenarios \|
	\| `MANIFEST.json` \| a hash of every file, and the capture harness recorded inside every frame set \|

	## How the pieces fit

	A frame set is a rendered approach at one sun altitude: images, the range to the conflict
	point at each pose, and a stamp saying what made it. The stamp records the map, the cloud
	cover, the settle time, the determinism rules digest, the texture streaming and quality
	settings, and the order the knots were captured in. That last field exists because capture
	order silently ruined a dark endpoint once and nothing recorded it.

	The networks are what the certificates are computed on. Each certificate in the code
	repository names its network by hash, and those hashes are in `MANIFEST.json`. If they do
	not match, the certificate and the network are not a pair and nothing may be concluded from
	them together.

	## Using them

	Clone the code repository, put these directories at `results/captures/` and
	`results/models/`, then:

	```bash
	python -m study.status # where the study stands
	python tools/family_fidelity.py # how much light each piece of the range spans
	```

	Both run without a simulator.

	Training reproduces exactly from these frames. Four separate runs gave byte-identical
	checkpoints. So the networks here can be regenerated from the frames here, and the
	certificates from the networks. The chain is only broken if the frames are lost.

	## What the study found

	Three policies differing only in which light levels their training frames came from. All
	three pass all three lighting conditions the standard tests, ten times out of ten, so the
	standard's own procedure cannot tell them apart.

	Driven at 65 light levels between those conditions, three repetitions each: the policy
	trained on the whole range crashes 0 times, and the policy trained on the regulatory test
	points crashes 21 times on the lead-vehicle scenario and 9 on the crossing pedestrian. A
	certificate names those light levels without simulating them.

	Method and results: `AD-Assurance-Lab/formal-verification--aeb--code`, `docs/STUDY_REPORT.md`.