Perceived precipitation intensity prediction model based on simultaneous dynamic and static observations for evaluating weather impacts on vehicle applications
Bibliographic record
Abstract
Adverse weather conditions increase road risks; thus, weather testing is necessary to evaluate vehicle performance. Outdoor testing is the most realistic, but it is not as controlled, repeatable, and rapid as indoor testing using an artificial precipitation system. However, outdoor data is still desirable for establishing the simulation targets on the vehicle surfaces. The dynamic-to-static precipitation intensity ratio is a useful parameter to correlate natural precipitation with perceived precipitation experienced by the moving vehicle. Theoretically, the amount of precipitation experienced by a translating surface depends on the orientation and travel speed. However, there are other external factors that could affect the perceived intensity, such as wind, turbulence, and droplet size distribution (DSD). Therefore, the existing simplified models evaluating a number of droplet strikes or precipitation flux calculated using natural precipitation density fail to have accurate predictions of the perceived precipitation rate, which hinders the evaluation vehicle application performance, such as sensor perception. In the present work, a semi-empirical prediction model is developed from the physics of precipitation in the context of vehicle aerodynamics and atmospheric dynamics. This model is validated with outdoor testing on a track for three days with rainy conditions. Multiple optical disdrometers are used to evaluate the precipitation rate experienced by a moving vehicle at different surface orientations through meteorological observations obtained in real-time from a nearby stationary meteorological tower and a moving vehicle. The data acquisition and processing methods are presented in detail. Results suggested that the proposed model is found to improve the current simplified mathematical expressions and is repeatable. It is found that improvements in prediction accuracy of perceived precipitation intensity compared to existing methods are usually more than 50%.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".