Simulation-Based Evaluation of Variation in Left-Turn Paths in the Coordinated Intersection Management
Bibliographic record
Abstract
Coordinated intersection management (CIM) has gained more attention with the advance of connected and autonomous vehicle technology. The optimization of passing schedules and conflict separation between conflicting vehicles are usually conducted based on the predefined travelling paths through the intersection area in the CIM. In real-world implementation, however, the diversity of turn paths exists due to multiple factors such as various vehicle sizes and automation control algorithms. The aim of this paper is to investigate how the variation in left-turn paths affects the feasibility and viability of optimal passing schedules, as well as the safety and efficiency of intersection operation. To do this, we start with identifying six typical left-turn paths to represent the variation. A scenario-based simulation is first conducted by using each of the paths as the nominal path. The optimal schedules and the corresponding alternative schedules are generated to calculate indicators for nominal performance, average performance, and robustness. The best path is selected in terms of schedule optimality and robustness. With schedules obtained by solving CIM models using the selected path, the left-turning CAVs are assumed to travel along one of the six paths randomly to simulate the path divergence. A surrogate safety measure, PET, is utilized to assess the safety of the intersection under CIM. The theoretical PET with the nominal path and the actual PET with the random path are calculated for each conflict event. Comparisons of two PET sets show the increase in conflict risk and vehicle delay. The conclusion can be drawn that the variation in left-turn paths causes the decline in safety level and travelling efficiency and should be considered in the CIM model to ensure safe and efficient implementation in the intersection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".