Perceived rain dynamics on hydrophilic/hydrophobic lens surfaces and their influences on vehicle camera performance
Bibliographic record
Abstract
Cameras are increasingly used in modern vehicles equipped with advanced driver assistance systems (ADAS) to collect environmental information. Cameras suffer performance degradation when driving in adverse weather conditions, such as rain, as precipitation droplets impact the camera lens and cause obstruction and blurring of the vision. The relationships between image quality, object detection accuracy, and surface wettability of camera lenses are investigated. This paper applies a previously developed evaluation procedure for wind tunnel testing with simulated adverse driving and rain conditions. Realistic rain characteristics perceived by a moving vehicle at different driving speeds are simulated using a novel rain simulation system implemented into a wind tunnel. Moreover, an emphasis is put on comparing the use of hydrophilic and hydrophobic surfaces to provide insights into material selection when designing camera lenses for ADAS. It is found that droplet dynamics, such as size, velocity, shape, and motion can impact the camera image quality and, subsequently, object detection accuracy. This paper demonstrates the use of various materials and evaluation metrics and their implications from a practical perspective when subjected to realistic driving-in-rain scenarios. The results suggest that the use of hydrophobic lenses promotes better performance over hydrophilic lenses in most cases with exceptions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".