Investigation of Behavioral Adaptation to Lane Departure Warnings
Bibliographic record
Abstract
Behavioral adaptation describes the collection of behaviors that occur after a change in the road traffic system. Typically, those behaviors not intended by the initiators of the change having a negative impact on safety are of particular interest. Although behavioral adaptation is frequently cited as an explanatory variable for observed discrepancies between engineering estimates and actual outcomes of safety interventions, a thorough understanding of behavioral adaptation does not, at present, exist. Most theories posit that a driver’s goal to maintain an acceptable level of risk will determine if and when behavioral adaptation will occur; few models incorporate individual driver characteristics into their explanation of behavioral adaptation. Recently, a qualitative model of behavioral adaptation was proposed. The model predicts that the degree of behavioral adaptation to a novel road safety intervention depends on several psychological characteristics of the individual, including propensity to trust automation, “locus of control,” and inclination toward sensation-seeking. To test the predictions of this model, simulator and test-track studies were conducted to investigate the ability of lane departure warnings to induce behavioral adaptation in drivers performing a secondary number-entry task. While the presence of reliable warnings in both settings improved lane-keeping performance, drivers tended to report a high degree of trust in both accurate and inaccurate systems, despite the intentional infidelity of the latter. Externals and low sensation-seekers were more likely to report an increase in trust in the system, regardless of its accuracy. The collective results from both studies indicate that, because of the propensity of some people to trust unreliable or faulty devices, caution should be used in attempting to predict the aggregate safety benefits of these systems.
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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 | 0.003 |
| 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 | 0.001 |
| 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".