Automated Decision System to Exploit Network Diversity for Connected Vehicles
Bibliographic record
Abstract
In this work, we introduce a methodology that takes advantage of the inherent network diversity present in vehicular communications to improve the performance of safety applications. This methodology is based on a framework that simultaneously exploits the strengths of each individual network by using a set of decision rules. The implementation begins with a manual approach in which a typical, hierarchical decision tree characterizes the decision process of a single application when sending data to other users in the network. Analytical and simulation results validate the decision system approach when diversity is exploited as demonstrated by a boost in application performance, achieving an average latency under 100 ms and a 40% increase in throughput due to the increased packet delivery ratio. We then apply an ensemble learning technique, Random Forests (RF), to automatically reproduce the performance of the manually built tree system. Simulations under realistic traffic scenarios show the RF approach can replicate manually-built tree performance with up to 98% precision. A comparison with another state-of-the-art hybrid method also shows the RF scheme improves performance under a different application scenario without additional manual adjustments. With our methodology, we can add different application requirements and network characteristics to obtain a fully automated and adaptable decision system to optimize vehicular safety applications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".