Hypervisor-Mediated Co-Design of Cybersecurity and Functional Safety in Vehicle E/E Architectures: A Cross-Domain Assurance Framework
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.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".