Exploring Realism in Virtual Testing: Towards a Scalable Platform Using Open-Source Solutions for Automated Driving Systems
Bibliographic record
Abstract
This paper presents a novel solution to the demand of a realistic virtual test environment (VTE) for the development and safety assurance of Automated Driving Systems (ADSs). The current VTE offerings suffer limitations when it comes to creating a rich environment (from the Operational Design Domain perspective) based on the ASAM OpenDRIVE files (a formatted description of the scenery), and hence there is no automatic OpenDRIVE scenery generation available that resembles the richness required to thoroughly test an ADS in a virtual environment. Therefore, through the use of Unreal Engine, leveraging the power of esmini’s roadmanager and developing on a primitive OpenDRIVE plugin, a comprehensive scenery with complex lighting, dynamic environmental conditions and photoscanned meshes can be achieved. There is then a demonstration of how this is incorporated into an end-to-end testing framework, using just one user interface to take the user from the creation and retrieval of scenarios through to the execution.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".