Assessment of the Impact of Network Infrastructure Failure on Infrastructure-supported vs. Infrastructure-Less V2X Systems
Bibliographic record
Abstract
With the increasing market penetration of connected and autonomous vehicles (CAVs), there will be a transformative impact on traffic flow dynamics, safety measures, and mobility management systems. Emerging technologies, such as CAVs combined with communication strategies, can promote efficient traffic management and mobility without solely relying on network infrastructure. This paper offers a comparative study of infrastructure-supported systems against vehicular communication-based systems with minimal reliance on existing infrastructure. Throughout this study, the latter is referred to as ‘infrastructure-less systems’. The conventional infrastructure-supported systems, although effective, are susceptible to infrastructure failures. The findings indicated that major network communication and operational delays, as well as reduced mobility and safety, were observed due to non-recurring failures within an infrastructure-supported system. The developed infrastructure-less-based network management system was tested using AIMSUN microscopic traffic simulator, where low latency and packet loss ratios were observed whilst maintaining mobility of the freeway even at peak travel times. These test cases help to leverage the shift to minimal cellular network reliance without sacrificing performance.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".