State-of-the-art virtualisation technologies for the centralised automotive E/E architecture
Bibliographic record
Abstract
The automotive industry is undergoing profound changes, driven by the need for safer, more environmentally friendly, and more accessible future mobility and transport systems for goods and people. Enabling technologies include electrification, digitalisation, and automation of future vehicles. These technologies are powered by a multitude of onboard Electronic Control Units (ECUs). A typical modern vehicle has about 100 physical ECUs to enable various aspects of its function. These legacy many-ECU electronic/electrical (E/E) architecture models, known as distributed E/E architecture, are deemed inefficient as the number of ECUs and their processing power requirements keep increasing. In contrast, emerging centralised E/E architectures propose using fewer physical high-performance onboard processors on which an almost unlimited number of virtual ECUs can be created to handle various legacy and modern applications. As a result, virtualisation techniques, which enable multiple virtual ECUs with different operating systems to run concurrently on a single hardware platform, are promising models for modern centralised E/E architectures. Motivated by this trend, this paper provides a structured and comprehensive state-of-the-art review of virtualisation techniques for automotive applications, covering areas such as resource allocation, AUTOSAR, peripheral I/O interfaces, and in-vehicle communication networks. We comprehensively review the literature and identify research gaps in virtualisation techniques for cache management, paravirtualsation, software-defined networking for in-vehicle networks, and virtualisation for enhanced prototyping and testing in the context of modern E/E architectures for modern vehicles.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| 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".