In Search of Severity Dimensions of Traffic Conflicts for Different Simulated Mixed Fleets Involving Connected and Autonomous Vehicles
Bibliographic record
Abstract
This study aims to estimate the severity of conflicts that may arise from the introduction of connected and automated vehicles (CAVs) by examining the vehicle paths generated by microsimulations of mixed fleets of human-driven vehicles and CAVs with different levels of automation (L1-L4 vehicles). The study assesses the severity of conflicts using a holistic approach that considers three dimensions: (1) proximity to collision, via the time-to-collision (TTC) indicator; (2) potential consequences of a conflict, via single surrogate safety measures such as maximum speed (MaxS) and vehicle speed difference (DeltaS); and (3) a combination of both dimensions to assign severity scores, via TTC and velocity vectors. The study’s findings suggest that moderate penetration rates of L3 and L4 vehicles (35–55%) show significant differences in the number of traffic conflicts with varying TTC values. Additionally, high penetration rates of L3 and L4 vehicles (above 55%) result in lower values of conflict consequences measures such as MaxS and DeltaS. Furthermore, the study shows that conflict consequences decrease if the follower is a L3 or L4 vehicle. The study’s findings also reveal that there is a considerable reduction in high severity conflicts when the penetration rate of CAV levels reaches 50%, and the full operation of L4 vehicles results in a 75.5% reduction in high severity conflicts. Therefore, this study provides valuable insight into the potential severe conflicts during the transition period from manual vehicle operation to full CAV operation. Overall, the study’s findings highlight the importance of assessing the severity of potential conflicts arising from the introduction of CAVs. By considering the proximity to collision and the potential consequences of conflicts, the study provides a comprehensive assessment of the severity of conflicts. This information can inform the development of policies and strategies to ensure the safe and responsible introduction of CAVs into our transportation systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".