From da Vinci to cybersecurity: tracing the evolution of autonomous vehicles and ensuring safe platooning operations
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
Abstract Since Leonardo da Vinci’s creation of a self-propelled cart in the 1500s (Palmer. in Significant figures in world history p. 75--7, 2018), the evolution of Autonomous Vehicles (AVs) has aimed to revolutionize transportation. While AVs promise improved safety, traffic efficiency, and industrial optimization by reducing human intervention, ensuring their security remains paramount. This paper provides a thorough literature review spanning from historical milestones to contemporary advancements in AV technology. It delves into the significance of Vehicular Ad-hoc NETworks (VANETs) for safety applications and underscores the critical role of speed harmonization and string stability in safeguarding AV platoons. Furthermore, the paper addresses cybersecurity threats targeting platoon networks, advocating for research into encryption mechanisms, road-side units, control algorithms, hybrid communications, and on-board system security to bolster communication security within platoons. By advocating for a balance between AV technological advancements and robust security measures, this paper facilitates safe and reliable AV platooning operations.
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