Scenario Co-Design for Systemic Evaluation of Connected and Automated Mobility Setups
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
Connected and automated vehicles are today at various stages of development. Due to their transformative potential, both on the existing transport system and on urban spaces, it is essential to investigate their impacts from a systemic perspective. A multi-factor evaluation cannot be based only on experimental setups. Projections of up-scaled, operational connected and automated mobility (CAM) services are required. In this article we propose TRESSY, a scenario-building approach for CAM service up-scaling. TRESSY follows a four-step model that aims to generate mid-term projections of relevant services based on foreseeable technological and infrastructure developments. We applied TRESSY in a multi-stakeholder CAM pilot project where experts collaborated on the design of a range of up-scale service scenarios and their associated technical systems and infrastructures. The results obtained show how TRESSY can facilitate the collaboration between heterogeneous stakeholders working on representative niche, critical technical systems of future mobility.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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