Evaluating weather impact on vehicles: a systematic review of perceived precipitation dynamics and testing methodologies
Bibliographic record
Abstract
Abstract The performance of road vehicles degrades when driving in adverse weather conditions. Weather testing for vehicles is important to understand the impacts of precipitation on vehicle performance, such as driver visibility, autonomous sensor signal, tire traction, and structural integrity due to corrosion, to ensure safety. This tutorial summarizes the essential elements for performing realistic testing by applying physical and meteorological rationale to vehicle applications. Three major topics are identified as crucial steps for precise quantitative studies, including understanding the natural precipitation characteristics, estimating the perceived precipitation experienced by a moving vehicle, and selecting data collection strategies. The methods used in current practices to investigate the effects of rain and snow on road vehicles at common facilities of outdoor test tracks, drive-through weather chambers, and climatic wind tunnels are summarized. The testing techniques and relevant instrumentations are also discussed, with considerations of factors that influence the measured data, such as particle size distribution, precipitation intensity, wind-induced droplet dynamic events, accumulation behaviour, etc. The goals of this paper are to provide a tutorial with guidelines on designing weather testing experiments for road vehicles and to promote the idea of establishing standardized methodologies for realistic vehicle testing that facilitates accurate prediction of vehicle performance in adverse weather conditions.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".