Design and Development of a Digital Twin Platform for Scenario-Based Testing of Road Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There currently exists a need for a platform that provides users with the ability to perform extensive, repeatable and meaningful simulation and testing for the hardware and software which compose vehicle/autonomous vehicle systems whilst being broadly accessible, widely supported and provides robust features and development tools. The contemporary implementations of similar systems are either financially exorbitant or highly contained. The system reflected in this paper aims to fill a gap in the industry of vehicle/autonomous vehicle development by extending on currently existing open-source software to provide a highly streamlined platform to support the production of general road vehicle and autonomous vehicle driving systems. The software tools and hardware components chosen for the system will be discussed, followed by the features constructed throughout the development process. The end result of the system is a platform that allows for quick, repeatable, accurate, and nearly endless testing of a digital twin of real life vehicles. This system will allow users to gain valuable simulation and testing data of hardware and software components in a manner which is not always feasible using the traditional methods of autonomous vehicle testing.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it