Deep-Racing: An Embedded Deep Neural Network (EDNN) Model to Predict the Winning Strategy in Formula One Racing
Bibliographic record
Abstract
This paper presents an embedded deep neural network model to predict the driver rank and the optimum pitstop strategy.In formula one racing, the race strategy is critical to determine optimal pitstops and finish the race in the best possible position.Considering a system with only one racing car, the pitstop can be decided just by looking at the degradation of the tires.But in reality, the formula one environment is more complex, and multiple probabilistic factors (like safety car phases, opponent strategy, and overtaking) influence the pitstop decision.Deep-Racing is a prediction and decision algorithm for formula one racing cars that uses neural networks with embedding layers.The algorithm is developed after carefully reviewing formula one racing and appropriate statistical modeling techniques, which can be trained for pre-race and real-time predictions during the race using the data from previous laps.Deep-Racing has the potential to help team principals and race engineers to decide the optimized strategy for making pitstops.It is trained on the data from seasons 2015-2022.This project is the first to utilize an embedded layer in motorsport racing predictions, and the results show an improvement in predictive accuracy compared with the previously available literature.This paper significantly expands the previous research in this field and proposes trends in the data available from the latest seasons.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".