The Longitudinal Driving Behavior of a Vehicle Assisted with Lv2 Driving Automation: An Empirical Study
Bibliographic record
Abstract
As the number of automated vehicles in our transportation system increases, it becomes increasingly important to understand how automation affects their driving behavior. This study defines and tests a methodology based on optimization methods to incorporate the longitudinal driving behavior of automated vehicles in the Wiedemann 99 car-following model. A pilot study was recently conducted in Portugal using a Mercedes-Benz of 2017 assisted with level 2 driving automation to gather empirical data. In total, 61 car-following events were used to support the calibration and validation tasks. The calibration error sustains the methodology’s descriptive capability to simulate the driving behavior of AVs, and the validation error sustains that the calibrated model parameters can reproduce the dynamic driving behavior of AVs with reasonable consistency and robustness. A total of seven model parameters were estimated and are in line with the trends often described in the literature on automated vehicles but also highlight differences that can be explained by different development and deployment strategies. Nevertheless, since empirical data from automated vehicles are hard to get, the presented work findings are also valuable for improving and validating future modeling efforts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".