State-of-the-art virtualisation technologies for the centralised automotive E/E architecture
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
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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