A Prospective Cloud-Connected Vehicle Information System for C-ITS Application Services in Connected Vehicle Environment
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
In the era of the Internet of Things (IoT), various applications providing people with utilities are rapidly emerging by the needs for people. Recently, combining cloud computing, IoT technologies, and vehicular applications promotes Intelligent Transportation System (ITS). In other words, this is for safety of vehicles and drivers as well as convenience of the drivers. Vehicular Ad-hoc Network (VANET) is an application of Mobile Ad-hoc Network (MANET), which is a networking technology including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) using wireless communications. In real life, vehicles and infrastructures which have a lot of sensors generate various data for Cooperative-Intelligent Transportation System (C-ITS) application services according to each sensor type. Therefore, collecting, processing, and storing a number of data generated from various sensors built in vehicles and infrastructures require a great computing capacity and storage resources. In this paper, we propose an architecture of prospective cloud-connected vehicle information system for C-ITS application services in connected vehicle environment and describe the procedure of our local and global vehicle information system concerned with case scenario.
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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 | 0.001 | 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