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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".