In Pursuit of Emergency Procedures for Automated Driving System-Involved Scenarios
Bibliographic record
Abstract
As automated driving technology becomes more widely deployed, it is imperative to determine how such operations may impact public safety officials’ interactions with these vehicles. The current study collected responses from 79 public safety officials (i.e., representatives from law enforcement, fire and rescue, and emergency medical services [EMS]) from 22 states in the United States of America (USA) and three Canadian provinces. Participants were surveyed during focus groups and personal interviews with regard to six vehicular scenarios they typically encounter (incident response, scene security, direction and control of traffic, traffic stops/checkpoints, attending to abandoned/unattended vehicles, and vehicle stabilization/extrication) to explore: (1) Their standard protocols in approaching manually driven (civilian) vehicles in these scenarios and (2) How such protocols would necessarily change when approaching an automated driving system (ADS) operating in driverless mode. This exploratory study found that the majority of participant responses (59) focused on the need to know how to disable an ADS-equipped vehicle. Participants also indicated there could be benefits relative to ADS operating in driverless mode, including a reduced number of traffic stops, safer traffic control, and the potential to convey more information when responding to a scene. The study ultimately provides a foundation upon which future studies could build in consideration of automated-vehicle design and enhanced safety operations relative to public safety officials.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".