Preface for Feature Topic on Human Driver Behaviours for Intelligent Vehicles
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
With the advancement of sensing, machine learning, and computing systems, automated driving applications have been growing rapidly worldwide. Together with the development of communication technologies such as dedicated short-range communication, extensively emerging intelligent vehicles have been developed to connect with vehicles, pedestrians, infrastructures, and clouds in the transportation network. Thus, intelligent vehicles have become intelligent mobile terminal that carries rich functions and services, which expand and deepen the scope of human–machine interaction between human drivers and intelligent vehicles in the intelligent cockpit. Human drivers are the center of intelligent vehicles. To make future vehicles trustworthy in driving safety, acceptable in social travel efficiency, and comfortable in the driving experience, developing technologies based on human drivers’ reliable knowledge and cognitive intelligence together with smart operation is an essential and promising solution. However, there are many challenges to be addressed including real-time human driver perception, adaptive regulation of inappropriate driving operation, safe and comfortable interaction between human drivers and intelligent vehicles intelligent cockpits, etc.
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 | 0.000 | 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 it