Hypervisor-Mediated Co-Design of Cybersecurity and Functional Safety in Vehicle E/E Architectures: A Cross-Domain Assurance Framework
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 rise of embodied intelligence in autonomous vehicles, driven by software-defined capabilities, demands a fundamental shift in their underlying Electrical/Electronic (E/E) architectures. While centralized architectures promise the computational power for advanced autonomy, they introduce critical challenges in ensuring safety and cybersecurity. The consolidation of mixed-criticality functions on shared hardware creates significant risks of fault propagation and vulnerability to cyber-attacks. This paper proposes a hypervisor-driven frame-work that provides a resilient foundation for intelligent vehicles. We present an E/E architecture that leverages a Type-1 hypervisor to enforce hardware-level isolation between functions. By partitioning the system into independent virtual machines, we safely co-locate ASIL-D (Automotive Safety Integrity Level) tasks with general-purpose applications, preventing interference and containing threats. Our evaluation demonstrates that this partitioned architecture improves fault coverage by 21.4% compared to non-isolated approaches, significantly enhancing system resilience. This approach ensures that the vehicle’s intelligent systems operate safely, securely, and deterministically, even under complex, dynamic conditions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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