Driving Mode Advisory for Emergency Maneuvering in TransVerse Enabled Connected Vehicular Network
Bibliographic record
Abstract
The future intelligent transport systems (ITSs) promise to improve traffic safety and security along with reducing the driver workload. In this article, we consider a pre-emptive emergency situation, wherein we design a safe-driving maneuver problem for transportation Metaverse (TransVerse) to compensate for driver reaction delay. The proposed scheme balances the risk of collision, and the spectral and computation resources required by the network. This combination of the spectral and computation resources required is also known as utility of the vehicular network. Further, an emergency maneuver is proposed to suggest a lane-changing maneuver based on the safe lane quality index for vehicles in vicinity of the emergency vehicle, also known as collision risk vehicles (CRVs). Moreover, based on the current position of the vehicles and the proposed safe maneuver, we propose a driving mode advisory to the drivers to provide a speed profile and a real-time high-level driving modes advice on acceleration, cruise, and engine brake. We evaluate the performance of the proposed safe-maneuver planning for CRVs in terms of collision probability, velocity, driving mode, and end-to-end lane changing delay. It can be observed that with the use of the proposed scheme, there is no effect on the performance of the high-CRV (HCRV). However, for the second following vehicle (HCRV2), the probability of collision decreases by 34.78%, and the velocity of the vehicle increases by 61.70% with the use of TransVerse. Moreover, a tradeoff between resource allocation and end-to-end delay of the network is also analyzed with and without TransVerse scenario.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".