Asynchronous Time-Sensitive Networking (TSN) Implementation in Automotive Zone-Based Architecture
Bibliographic record
Abstract
In the realm of modern automotive technology, vehicles now incorporate a range of advanced features, such as Advanced Driver Assistance Systems (ADAS), infotainment systems, and autonomous driving technologies. In response to the complexities of wiring and the imperative for improved network performance, a shift is occurring from conventional domain-based In-Vehicle Network (IVN) architectures to zone-based configurations. Similarly, there’s a transition from traditional IVN protocols to Ethernet-based alternatives. Additionally, the adoption of Time-Sensitive Networking (TSN) is increasing, allowing for deterministic data transmission via Ethernet networks. The combination of TSN with zone-based architectures holds the potential for optimizing data transmission and reducing End-to-End (E2E) delay its variation (Jitter), hence ensuring the desired service level agreement (SLA) and enhancing the quality of experience (QoE). Our contribution involves simulating an IVN architecture where diverse Electronic Control Units (ECUs) are segregated into zones and interconnected via Ethernet. Our work includes implementing Asynchronous Traffic Shaping (ATS) standard in TSN for improved experiments with different ATS parameters and analyze their impact on E2E delay and jitter. The results demonstrate the efficiency of TSN, specifically ATS, in the IVN environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".