Risk, Readiness, and Return: Rethinking the North American Connected Vehicle Deployment Strategy
Bibliographic record
Abstract
Successful adoption of a new technology often relies as much on effective policy as it does on technical contributions. Despite near universal agreement that mandatory deployment of connected vehicle (CV) technology and the exchange of vehicle position and status information through Basic Safety Messages (BSMs) could significantly improve road safety, reduce traffic congestion, and streamline commercial vehicle operations, North American lawmakers have not yet been persuaded that the benefits outweigh the deployment challenges. To date, proponents have focused on the possibility of using the technology to prevent road accidents and traffic fatalities by drawing a driver’s attention to hazardous situations and instigating corrective action. Here, we argue that extending the use of BSM data to road safety analysis and traffic management considerably enhances the value proposition and makes the case for mandatory deployment even more compelling. We further argue that a three-stage approach to connected vehicle technology deployment that prioritizes low-risk but high-readiness applications will maximize return to stakeholders, minimize operational risk, and increase confidence in the technology. Finally, we argue that conventional approaches to assessing technology readiness are not adequate here, and propose an alternative way to assess deployment readiness.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".