Emergence of an Autonomous Vehicle Secondary Data Market for Breakthrough Applications
Bibliographic record
Abstract
The prophesied circulation of fleets of autonomous vehicles (AVs) in urban and rural environments promises unprecedented opportunities to remotely sense streetscapes at fine-grain spatial and temporal resolution. AVs employ a variety of on-board sensors to capture information about the local environs for the primary purpose of vehicular navigation. However, we propose that these data may find further secondary use in a broad array of breakthrough applications: technologies and use cases that are enabled through the fine-grain spatio-temporal sensing of the lived environment. Consequently, a market for the secondary use of AV-collected data is emergent and a cloud-based architecture to manage the collection, processing, and communication of AV-derived data is required. Excitingly, the application of machine learning models to extract desirable secondary information from these fine-grain spatio-temporal data will enable unprecedented global-scale and time-series studies. Herein, we outline our vision for the utility of a Remote sensing AV-based Informatics Layer (RAIL) and the breakthrough applications it would enable. We define our vision based on recent and relevant trends in AV technology, discuss anticipated applications, discuss key technical considerations, and explore theoretical economic models for the exposed API. We conclude with discussion of the socio-technical ramifications of this system.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".