In-Vehicle Passing Collision Warning System for Two-Lane Highways Considering Driver Characteristics
Bibliographic record
Abstract
Passing collisions are one of the most dangerous traffic safety problems. These head-on collisions occur when the driver of the passing vehicle is distracted or does not assess the situation appropriately. The purpose of this study is to develop a passing collision warning system (PCWS) for drivers on two-lane highways to prevent passing collisions and improve road safety. This paper presents a framework and algorithm design for a PCWS that ensures that drivers have an adequate sight distance for a safe passing maneuver. The system uses an available radar sensor to detect the closest opposing vehicle traveling in the left lane and calculates its position, speed, and acceleration rate to estimate the time to collision and compare it with the time required for the passing vehicle to clear the lane. Realistic initial time and passing time models were established using actual experimental field data collected using a global positioning system (GPS) data logger device that was installed in the passing, impeding, and opposing vehicles and used to record the position and speed of different passing vehicles at 1-s intervals. The MATLAB simulation was developed and used to replicate real-life passing maneuvers and was also used to create the algorithm for the proposed warning system. The passing maneuver parameters were selected from probability distribution curves based on field data. The simulation model determines the relative distance and speed of the opposing vehicle at four different time intervals. The different factors that impact system accuracy were also examined.
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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".