Integrating Vehicle-to-Infrastructure Communication for Safer Lane Changes in Smart Work Zones
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As transportation systems evolve, ensuring safe and efficient mobility in Intelligent Transportation Systems remains a priority. Work zones, in particular, pose significant safety challenges due to lane closures, which can lead to abrupt braking and sudden lane changes. Most previous research on Connected and Autonomous Vehicles (CAVs) assumes ideal communication conditions, overlooking the effects of message loss and network unreliability. This study presents a comprehensive smart work zone (SWZ) framework that enhances lane-change safety by the integration of both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. Sensor-equipped SWZ barrels and Roadside Units (RSUs) collect and transmit real-time hazard alerts to approaching CAVs, ensuring coverage of critical roadway segments. In this study, a co-simulation framework combining VEINS, OMNeT++, and SUMO is implemented to assess lane-change safety and communication performance under realistic network conditions. Findings indicate that higher Market Penetration Rates (MPRs) of CAVs can lead to improved lane-change safety, with time-to-collision (TTC) values shifting toward safer time ranges. While lower transmission thresholds allow more frequent communication, they contribute to earlier network congestion, whereas higher thresholds maintain efficiency despite increased packet loss at high MPRs. These insights highlight the importance of incorporating realistic communication models when evaluating traffic safety in connected vehicle environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it