A modular distributed simulation‐based architecture for intelligent transportation systems
Bibliographic record
Abstract
Summary Simulations have been used extensively for evaluating scenarios, which are very difficult, costly or impractical to implement in real systems. Testing in a synthetic, realistic environment provides a means to determine the viability of solutions. Simulations have proved to be very useful in the verification of algorithms and protocols, offering tools for testing them in different situations. The simulation of vehicular area networks pose additional challenges as realistic mobility models are crucial and must be incorporated in scenario elements while applications and communication protocols are tested. Several simulators and simulation frameworks have been designed that aim to synthetically reproduce communication and mobility of vehicles as realistically as possible. The majority of such simulators merge pre‐existing networking and mobility simulators, which add issues regarding compatibility and realism. Such simulators present limited run‐time 3D visualization tools, essential for providing immersive environments. Therefore, in this paper, we propose real‐time simulation and 3D visualization for vehicular networks of realistic scenarios. This proposed simulation system generates output in real time, making use of 3D‐modelled real‐world maps and effectively generating visualization as elements are updated in the simulation. Experiments have been conducted with simulation and visualization components to evaluate delays and performance of the proposed simulator. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".