A Novel Micro-services Cluster Based Framework for Autonomous Vehicles
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
Modern technologies like digital spaces, intelligent transportation, and digital operations are faced with technical challenges related to data capturing, data processing, data storage, data security, communication, etc. Ensuring security and reliable message propagation among autonomous vehicles is a major challenging task. Establishing appropriate environment for developing vehicular-based solutions by the vehicle manufactures increases their estimated cost and time. Hence, to minimize this problem, this proposal aims to introduce Service Oriented Architecture (SOA) principles, to access trust based solutions as simple cloud based services by the clients. We propose a framework to integrate three mandatory vehicular applications namely trust estimation, secured message dissemination, and routing as cloud-based microservices. We also propose an innovative CipherText Policy Attribute Based Encryption (CP-ABE) algorithm to ensure confidentiality of data by an access control system in highly dynamic and automated network. The services are deployed as Docker images using advanced concepts of Dockers and Containers. Dockers coordinate the orchestration of multiple tasks related to the proposed microservices and help to implement the services in cross-platform environments. These services can be implemented in both autonomous and manual vehicular systems. The service providers can charge the clients based on the usage of the services. A detailed experimental analysis is accomplished to evaluate the performance of the proposed micro services in cross platform environments; further, an extensive simulation is performed to assess the individual performance of the proposed vehicular applications.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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