Rules Versus Risk: Why Perceptions of Pedestrian Comfort and Safety Differ for Interactions with Self-Driving versus Human-Driven Vehicles
Bibliographic record
Abstract
This study aims to inform the development of pedestrian-friendly self-driving vehicle (SDV) policies by investigating how perceptions of pedestrian comfort and safety are affected by SDV technology. We use structural topic modeling to investigate themes in open response survey comments from participants who viewed and rated short video clips of pedestrian interactions with motor vehicles. Although they were all human-driven vehicles (HDVs), participants were told that half of the interactions (randomly for each individual) involved SDVs. This deception-based survey design enabled isolation of the intrinsic effect of automated driving on comfort and safety perceptions across a broad sample of the population. Model results identified latent topics significantly more likely to be discussed for SDV versus HDV interactions, ceteris paribus. There is a greater focus on pedestrian responsibility to be cautious, aware, and predictable in interactions with SDVs than HDVs. Topics more associated with SDVs tend to focus on strict rule compliance (obligation fulfillment), whereas topics more associated with HDVs tend to focus on risk mitigation (with less focus on rules). Recommendations to mitigate potential negative impacts of introducing SDVs on the attractiveness of walking include requirements for conservative SDV operation in city streets, limiting potential interactions that require negotiated priority with SDVs (through physical separation and clear traffic controls), and enhancing external communication from SDVs.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".