Model Based Systems Engineering for Sustainable Autonomous Vehicle Design and Development
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".