Professional Driving Simulator to Design First-Time-Right Race Cars
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Professional driving simulators can be successfully exploited to shorten the traditional design-prototype testing-production process relative to a new race car.</div><div class="htmlview paragraph">Consider as a real example the Dallara 2014 Super Formula (“SF”) race car; built in 2013 at the Dallara factory in Varano de' Melegari, Parma, Italy and scheduled to race in Japan in 2014. Professional race drivers from the SF series have already been conducting multiple test sessions with the Dallara Simulator (Dec 2012), working together with vehicle dynamicists, aerodynamicists, designers, structural engineers and engine manufacturers, in an effort to evaluate and validate kinematics, steering geometry, aerodynamics, packaging, cooling, engine performance, as well as monocoque stiffness and minor installation details, even before the design had started.</div><div class="htmlview paragraph">Multiple on track test sessions (Jul-Sep 2013), conducted with the real prototypes, have impressed both drivers and their respective engineers and engine representatives. The real car was found to be close in performance and feeling with respect to the virtual car developed on the simulator.</div><div class="htmlview paragraph">This experience is a remarkable example worth sharing to the SAE community. It illustrates an innovative way for Motor Racing engineers to shorten the design-to-track development time.</div></div>
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".