Exploring Safe Overtaking Behavior on Two‐Lane Two‐Way Road Using Multiagent Driving Simulators and Traffic Simulation
Bibliographic record
Abstract
Safety and efficiency of autonomous driving behavior are a tradeoff. Behaviors that are too focused on safety can reduce road operation efficiency, while those that are too efficient can compromise passengers’ safety beyond their tolerance. Therefore, it is important to understand people’s characteristics and maintain a balance between safety and efficiency. Overtaking, which involves passing the preceding vehicle and improving road capacity, requires complex interaction as collisions with opposing vehicles must be avoided on a two‐lane, two‐way road. Overtaking to increase road capacity can induce unnecessary deceleration in oncoming vehicles, harming oncoming traffic flow. To address these concerns, a diverse dataset of natural overtaking behavior is a priority. We conduct experiments using a network connection between two multiagent driving simulators to collect a human behavior‐based overtaking dataset and develop driving behavior models engaged in overtaking situations using the Extra Trees model. The behavior models are embedded in microsimulation to generate human behavior‐based datasets under different conditions using a dynamic link library and component object model interfaces. To understand the interaction in an overtaking scenario by the generated datasets, we used a K‐means clustering technique to analyze the different reaction behaviors between the oncoming and overtaking vehicles. The threshold for achieving a balanced combination of safety and efficiency is established using XGboost. Finally, safe overtaking behavior is analyzed using a combination of the classified driving styles and thresholds. The results show that the overtaking vehicle can safely start overtaking without endangering oncoming vehicles when both speed and distance conditions are met simultaneously; the speed is lower than 44.29 km/h and it is 407 m away from oncoming 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".