Weather aerodynamic adaptation for autonomous vehicles: a tentative framework
Bibliographic record
Abstract
While autonomous vehicles (AVs) are potentially the future of transportation, one of the main issues that need to be addressed is their behaviour and response to adverse weather conditions. Herein, we proposed a research frame to understand and mitigate the impact of weather stressors (wind, rain, snow, ice, and fog) on AVs. A recently launched initiative to design and engineer an indigenous Canadian road vehicle served as a background for this intended framework. The proposed frame consists of ( i) on-road testing and numerical computational fluid dynamics (CFD) simulations to derive statistically significant critical weather conditions (weather design cases, WDCs) and ( ii) simulation of these weather conditions in the ACE climatic wind tunnel at Ontario Tech University, Canada, to ( iii) identify adaptive controls to minimize the effects of the WDCs on vehicles improving their aerodynamics, safety, and sensor functionality. This framework is intended to ( i) provoke discussions among the AV industry and research stakeholders in Canada and elsewhere and ( ii) provide a context for future research in related areas such as AV aerodynamics, maneuverability, weather impacts (e.g., wind, rain, snow, ice, and fog), sensors, and soiling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".