Study on the Impact of Autonomous Driving Technology on the Economy and Society
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 rapid development of the digital economy, autonomous driving technology, as a critical area of future transportation, is gradually transforming traditional transportation models and urban operation methods. This paper uses Baidu’s “Apollo Go” as an example to explore the impact of autonomous driving technology on the ride-hailing market, related industries, financial markets, and environmental protection. It also analyzes the employment impacts, safety risks, data privacy, and ethical challenges this technology brings. Through an in-depth study of the current application, market reactions, and social acceptance of autonomous driving technology, this paper concludes that while autonomous driving has significant potential to improve transportation efficiency, reduce costs, and lower carbon emissions, it also faces challenges such as labor market transitions and safety concerns. The paper suggests that the future development of autonomous driving technology depends on policy support, technological innovation, and social acceptance, and its widespread adoption will help promote the construction of smart cities and sustainable development.
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