Scheduling soft aperiodic messages on FlexRay in-vehicle networks
Bibliographic record
Abstract
The FlexRay communication protocol is expected to be the de-facto technique standard for the next generation high-speed networks on vehicles. A number of recent studies has thus investigated message scheduling techniques for FlexRay systems. However, most existing work focused on either the scheduling of periodic messages on the static segment or the scheduling of hard aperiodic messages on the dynamic segment while soft aperiodic messages have been neglected. Also, isolated scheduling severely limits the overall performance of all messages including periodic, hard aperiodic and soft aperiodic messages in terms of bandwidth utilization and transmission latency. In order to address these aspects, this paper presents an algorithm, referred to as Joint Scheduling Algorithm for FlexRay (JSAF), which jointly schedules soft aperiodic messages together with periodic and hard aperiodic messages in real-time FlexRay systems. The algorithm prioritizes periodic messages and hard aperiodic messages and first schedules them onto the static segment and the dynamic segment, respectively. Soft aperiodic messages are then dynamically scheduled with an online scheduler by utilizing unused time left by periodic messages and hard aperiodic messages. Performance evaluation results are presented to demonstrate the effectiveness and competitiveness of our approaches when compared to existing algorithms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".