Assessing the Impact of Vehicle-to-Vehicle Communication on Lane Change Safety in Work Zones
Bibliographic record
Abstract
Connected and automated vehicle (CAV) technology has the potential to enhance lane change safety in work zones, especially during lane closures. However, the safety implications of vehicle-to-vehicle (V2V) communication under realistic operating conditions remain insufficiently understood. This study investigates the impact of V2V communication on lane change safety in work zone scenarios using a calibrated co-simulation framework that integrates both traffic and communication networks. The framework simulates a range of realistic conditions–including varying market penetration rates (MPRs), communication ranges, and merge strategies (early and late)–and evaluates lane change safety using the time-to-collision (TTC) metric. A data dissemination algorithm is incorporated to coordinate V2V messaging and enable CAVs to initiate safe lane changes. Unlike prior studies that assume ideal communication conditions, this work simulates realistic V2V communication by incorporating metrics such as packet loss and packet delivery ratio to examine their impact on lane change safety. Findings indicate that higher MPRs and extended communication ranges generally enhance safety; however, limitations in communication quality can significantly reduce these benefits–particularly in late merge scenarios, where degraded data exchange decreases safety. Sensitivity analyses further reveal that lane-change timing and communication range are critical factors influencing safety outcomes, emphasizing the need to account for communication reliability when designing and evaluating CAV-based safety interventions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".