Adjustable automation and manoeuvre control in automated driving
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Current implementations of automated driving rely on the driver to monitor the vehicle and be ready to assume control in situations that the automation cannot successfully manage. However, research has shown that drivers are not able to monitor an automated vehicle for longer periods of time, as the monotonous monitoring task leads to attention reallocation or fatigue. Driver involvement in the automated driving task promises to counter this effect. The authors researched how the implementation of a haptic human–vehicle interface, which allows the driver to adjust driving parameters and initiate manoeuvres, influences the subjective experience of drivers in automated vehicles. In a simulator study, they varied the level of control that drivers have over the vehicle, between manual driving, automated driving without the possibility to adjust the automation, as well as automated driving with the possibility to initiate manoeuvres and adjust driving parameters of the vehicle. Results show that drivers have a higher level of perceived control and perceived level of responsibility when they have the ability to interact with the automated vehicle through the haptic interface. The authors conclude that the possibility to interact with automated vehicles can be beneficial for driver experience and safety.
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.
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 | 0.001 | 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) | 0.004 | 0.002 |
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 it