Modeling Passing Maneuver Based on Vehicle Characteristics for In-Vehicle Collision Warning Systems on Two-Lane Highways
Bibliographic record
Abstract
Modern vehicles are equipped with various sensors of high accuracy and sensitivity, based on which it is possible to implement passing collision warning systems (PCWS) for two-lane highways. In previous systems, the time required to complete the passing maneuver safely was formulated based on pre-established regression models. In this paper, this time is formulated based on actual vehicle characteristics. The new vehicle dynamics model for the PCWS prototype includes steering control and drivetrain models, and allows more accurate prediction of the required passing time. The geometry of the passing maneuver (for the case of an impeding truck), the main phases of the passing process, and the distances related to the conditions for predicting passing time are described. The interactions between the PCWS and driver actions are formulated. The steering control model is based on a two-dimensional perspective representation of the three-dimensional reality perceived by the driver. The drivetrain model, including inertial and mechanical losses in the drivetrain, considers automatic gear shift and the presence of a torque converter to simulate vehicle performance accurately. The proposed PCWS was tested using MATLAB Simulink.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".