Migrating to Ethernet-Based Centralized Automotive Architectures
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
Automotive Electrical/Electronics architectures of modern vehicles are currently going through a large shift from distributed to centralized systems. As the number of software features grows and the desire to be user-configurable is becoming ever more important, manufacturers are moving towards flexible centralized platforms. Many new technologies are being used to enable this shift to centralization, such as Automotive Ethernet. Using a workbench constructed with state of the art hardware as a testing platform, an investigation into the process of migrating Electronic Control Unit software from CAN-based distributed architectures into centralized architectures with CAN-FD and Automotive Ethernet as their primary networks was performed. An analysis of how Time Sensitive Networking (TSN) extensions can be used to provide real-time capabilities within automotive Ethernet networks is presented. Further, a partial assurance case is constructed for a system using TSN's Time Aware Shaper to provide time-triggered real-time communication. It is intended for use by system designers to assist in the safety analysis of systems using TSN.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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