Retracted: From Formula One to Autonomous One: History, Achievements, and Future Perspectives
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
This letter is the first report from a series of IEEE TIV's Decentralized and Hybrid Workshops (DHWs) on Intelligent Vehicles for Education (IV4E). The role of intelligent vehicles in promoting education for all ages through autonomous racing was discussed during a recent DHW. Over the past decade, autonomous racing has emerged due to advancements in self-driving technologies. While still focused on extreme speed, autonomous racing differs from conventional automobile racing in its development philosophy, as human drivers are no longer involved. The absence of human drivers should be regarded as a new chance to increase competitiveness and entertainment value. This letter discusses opportunities to promote education-oriented autonomous racing. Recall that the flagship car race is Formula 1, where “formula” denotes technical restrictions that should be satisfied strictly. We name the new race series Autonomous 1 or A1, leveraging the power of autonomous intelligence in education. The achievements made in Formula 1 and typical autonomous races are reviewed, followed by discussions about A1’s future perspectives. Specifically, A1 needs to maintain race consistency, update rules, and provide personalized commentary to support all-age education.
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.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".