Artifacts for the paper "Concretization of Abstract Traffic Scene Specifications Using Metaheuristic Search"
Bibliographic record
Abstract
This deposit contains measurement data and additional artifacts pertaining to the "Concretization of Abstract Traffic Scene Specifications Using Metaheuristic Search" paper. Specifically, the artifacts are divided into 6 directories: 0-config/ contains configuration files use to generate input scene specifications (using Scenic) with various number of actors. 1-data/ contains the generated input scene specifications for each map and actor size. It also contains additional scene specifications used for RQ3 (in the `zalaFullcrop/` subfolder). 2-results/ contains measurement results (i.e. (1) visualizations of generated scenes and (2) corresponding scene specifications containing exact positions of each vehicle, as well as (3) measurement data) for each research question. 3-figures/ contains figures derived from the contents of `results/` (including additional figures not included in the publication). 4-TestingSemanticSegmentation/ contains artifacts related to the integration of the CARLA simulator and of three computer-vision components: 0-sceneConfig/ contains configuration files used to generate scenes. 1-scenesWithExactCoordinates/ contains (1) visualizations of generated scenes and (2) corresponding Scenic-compatible scene specifications containing exact positions of each vehicle. 2-staticImages/ contains dashcam images for generated scenes and corresponding ground truth semantic segmentation, obtained through the CARLA integration. 3-predictions/ contains predicted semantic segmentation results (and their overlay on top of the corresponding dashcam image) obtained using three computer-vision components. 4-videos/ contains videos of generated scenes running with the default AV stack included in CARLA. 5-metrics contains initial measurement data for the predicted semantic segmentation. 5-simulationVideos/ contains videos of two scenario-based test cases executed in simulation. The two scenarios are only differenciated by the initial scene, which impacts the outcome of test execution (collision vs. no-collision). All the code of the proposed MHS-based scene concretization approach is implemented as an extension to the Scenic tool, which is available under the 3-Clause BSD License.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.371 | 0.124 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".