Automatic Driving Passage Strategies for Signal‐Free Pedestrian Crosswalks Using an Improved Responsibility‐Sensitive Safety Model
Bibliographic record
Abstract
Signal‐free crosswalks are a high incidence area for pedestrian–autonomous vehicles (AV) conflicts, but there is no comprehensive and reasonable solution for AVs to safely and efficiently navigate through these conflict scenarios. To address this problem, this study proposes a responsibility‐sensitive safety (RSS) model specifically for pedestrian–AV conflicts in signal‐free crosswalks. The model is based on the principles and contents of existing RSS models and proposes a safe AV access strategy for hazardous scenarios. The effectiveness of the strategy is verified by an integrated SUMO simulation taking into account the vehicle motion state, driving conservatism, and safety. The results show that the proposed automatic driving access strategy based on the improved RSS model effectively improves the driving stability and safety of the AV through the signal‐free crosswalk. This study provides a solution to the pedestrian–AV conflict in signal‐free crosswalks on road sections, which can provide a reference for the further promotion and application of the RSS model in the field of autonomous driving.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".