Multidimension Analysis of Autonomous Vehicles: The Future of Mobility
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
The level of investment in AVs technology has been increasing over the years as both researchers and developers are cooperating with the objective of developing AVs and understanding their behaviors and implications. Despite the enthusiastic speculation about AVs, little is known about the implications of AVs on our lives and the intertwined relationships between the implications. Thus, the main objective of this paper is to reveal the benefits and risks of AVs and sketch out the main trends in this area in order to provide some directions and recommendations for the future. This study focuses on analyzing the impact of AVs on the required fleet size, vehicle utilization, cost of mobility, public transit service, public behavior, transportation network, land use, economy, environment, society, and public health. Furthermore, the paper analyzes the intertwined relationship between the implications of AVs. Additionally, the paper sheds light on the potential benefits and challenges of the deployment of AVs in developing countries. The analysis shows that while AVs offer multiple benefits, they also pose new risks. The degree to which AVs can affect our plant mainly depends on regulatory actions, as the broader implications of AVs are mainly dependent on how the technology will be adopted, which can be controlled by regulatory actions. Doi: 10.28991/CEJ-SP2021-07-06 Full Text: PDF
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 | 0.001 |
| 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