On the Awareness of Connected Vehicles at Unsignalized Intersections
Bibliographic record
Abstract
In this paper, we use the Perceived Safety Analysis Framework (PSAF) to assess the awareness of vehicles performing an unprotected left turn at unsignalized intersections. PSAF is an analytical method developed to quantify the awareness of vehicles to surrounding safety-critical road users in traffic. We derive safety conditions for unprotected left turns using surrogate safety measures and right of way rules, and determine which road users are safety-critical to the left-turning ego vehicle. Then, we evaluate the Perceived Safety Error based on whether the ego vehicle can detect critical road users (CRUs) using sensors (such as camera or radar) and via vehicle-to-everything (V2X) communication. We demonstrate that for intersections with sparse traffic, vehicle-to-vehicle (V2V) communication may be insufficient for left-turning vehicles to get full awareness of CRUs, and vehicle-to-infrastructure (V2I) communication helps to eliminate awareness gaps.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".