Drive Risk Assessment Based on Game Theory Combinatorial Weighting—Unascertained Measure Theory
Bibliographic record
Abstract
The driving risk is assessed using the theory of unascertained measures to determine the presence of a conditional switch in the control system of a human‐machine codriving vehicle. Relevant risk indicators for driving are selected, including five driver‐related indicators and three vehicle‐related indicators. Subsequently, each indicator’s threshold range and associated risk level are analyzed and defined. The methodologies for establishing unascertained measure and their corresponding functions for both single and multiple indicator unascertained measure are then elucidated. A game theory–based weighting method is proposed, employing ordinal relationship analysis (ORA) and entropy weighting (EW) to determine indicator weights while utilizing confidence identification criteria to ascertain risk levels. Finally, experimental analyses are conducted on the driving risk assessment model, and the simulation results demonstrated the model’s ability to distinguish between normal and risky driving. In a continuous driving simulation, the model successfully identified a peak risk period (Level V) and, following a system alert, driving behavior returned to normal risk levels within 5 min. The model demonstrated utility for control switching decisions in human‐machine codriving scenarios, identifying instances where driver risk (Level IV) significantly exceeded vehicle risk (Level II), indicating a need to transfer control to the vehicle system. Consequently, the study’s findings can provide theoretical support for control switching mechanisms in human‐machine codriving vehicles.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".