Model Based Systems Engineering for Sustainable Autonomous Vehicle Design and Development
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
Model Based System Engineering (MBSE), introduced in the 2000s, has become a cornerstone for automobile companies like BMW, Toyota, and others prominently involved in the development of autonomous vehicles. MBSE is a unique systematic approach that uses designs and architecture instead of traditional document-centric methods. While the integration of MBSE in autonomous systems shows great promise for system development, there are still drawbacks due to the process of its complex integration. Currently, the engineering community is shifting its approach in systems engineering from document-based system engineering to MBSE. The shift has provided numerous advantages, one example being the enhancement of safety and security using Systems Modeling Language (SysML). Additionally, the continuous verification and validation of the system allowed by MBSE ensures that communication protocols meet real-time constraints. This study aims to address how MBSE can be used in autonomous vehicle development to improve functionality, secure connectivity, vehicle certification and enhance trust/confidence. Additionally, exploring how to overcome challenges such as streamlining existing requirements, test identification, navigating multi-perspective simulation, and improving vehicle-to-vehicle (V2V) communication. By using practical and multi-dimensional methods, formalisms, and applications, the future of MBSE shows great potential as a fundamental component to support effective, collaborative, and successful autonomous development environments.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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