Cooperative and Efficient Real-Time Scheduling for Automotive Communications
Bibliographic record
Abstract
FlexRay is an automotive network communication protocol. It provides support to transmit time-sensitive messages in automobiles. FlexRay transmits periodic messages in a static segment and a periodic messages in a dynamic segment. To improve transmission reliability, FlexRay offers hybrid data management schemes for both static and dynamic segments. However, existing approaches only schedule static segment and dynamic segment separately, leading to poor bandwidth utilization and transmission delay. Moreover, due to the bandwidth limitation, existing best-effort retransmission for all segments fails to achieve high reliability. To address these two concerns, we propose a novel and efficient scheduling scheme, called Coefficient. The idea behind Coefficient is to cooperatively schedule the static and dynamic segments, while judiciously stealing the selective slacks for reliable transmission based on practical fault models. Coefficient schedules both static and dynamic segments in the dual-channel manner based on practical fault models. Extensive experiments based on real-world case studies demonstrate that Coefficient meets the needs of both real-time transmission and reliability requirements, and delivers significant performance improvements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".