Behavioral Adaptation to Adaptive Cruise Control
Bibliographic record
Abstract
The ability of adaptive cruise control (ACC) to induce behavioral adaptation in drivers was assessed in a test-track environment. Eighteen experienced drivers performed a secondary, in-vehicle number search task while following a confederate lead vehicle. The three counterbalanced conditions were: No ACC (self-maintained average headway of 2 s), ACC-Short (headway of 1.4 s) and ACC-Long (headway of 2.4 s). Results indicate that ACC can induce behavioral adaptation in potentially safety-critical ways. Participants were able to correctly identify significantly more stock price quotes per minute when using ACC than when they drove unaided. At the same time, participants reacted more slowly to a safety-relevant brake light detection task when they used ACC, and responded within a safe time margin 33% less often. This effect was particularly pronounced in those scoring high on a sensation-seeking scale. ACC use was associated with impaired lane-keeping performance, an effect that was also more evident in high sensation-seekers. During a simulated failure of the ACC system, participants waited until the vehicle-to-vehicle headway was 0.6 s before they intervened; those with an external locus of control took longer to react than those with an internal locus of control. Finally, participants' trust in ACC increased following exposure, and was not affected by the failure of the ACC system. Results are consistent with similar research conducted on lane departure warning 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 | 0.000 | 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 | 0.000 |
| 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".