Research on Innovation Posture of Automated Driving Technology Based on Patentometrics
Bibliographic record
Abstract
As an emerging technology that integrates multidisciplinary and multi-technology, Automated Driving is triggering a new round of scientific and technological revolution and industrial transformation, thus attracting the attention of all countries in the world. Based on the Derwent patent database, this paper collects relevant patent information related to Automated Driving from 1998 to 2016 by using software tools such as patent measurement and CiteSpace, and empirically analyzes the temporal distribution, structural distribution, hotspot and frontier technologies, and geographical distribution of automated driving. The study found that automated vehicles have rapidly developing in the past 5 years, which indicates that the technical innovation of automated driving is in an emerging stage. There are geographical differences in innovative activities and capabilities in the field of automated driving. The United States has the absolute competitive advantages, while each country has different technological innovation capabilities in different technical fields. Finally, this paper puts forward some suggestions of our country to develop the technology and industry of automated driving in the future.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.038 | 0.049 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".