Investigating Improper Lane Changes: Driver Performance Contributing to Lane Change Near-Crashes
Bibliographic record
Abstract
We investigated the contributing factors that led to the lane change near-crashes recorded in the 100-Car Naturalistic Driving Study using a case-crossover experimental design. Drivers’ visual behavior and vehicle control were compared across a sample of lane change near-crashes and matched baselines. Baseline lane changes were sampled if they occurred prior to the near-crash, had a similar maneuver as the near-crash (including direction and speed), occurred within ± 2 hours from the time of day, occurred in similar light conditions, occurred on a similar day of the week (weekday vs. weekend), occurred on a road that had a similar number of lanes, had a similar placement of surrounding vehicles, and were made by the same driver. A total of 18 lane change near-crashes and 33 baseline lane changes were identified. Left lane change near-crashes appear to have resulted in part because drivers tended to slow down at the start of the maneuver and were less likely to use their rearview mirror. Right lane change near-crashes appeared to have occurred because of more aggressive maneuvering, infrequent turn signal use, and because drivers were less likely to look over their shoulder. Deficiencies in judging the distance and approach rate to adjacent vehicles, as well as circumstances in the environment, may also have played a contributing role.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".