Multi-task single lens for automotive vision applications
Bibliographic record
Abstract
The increasing trend to use vision sensors in transportation is driven both by legislation and consumer demands for higher safety and better driving experiences. Awareness of what surrounds an automotive vehicle can directly affect the safe driving and maneuvering of that vehicle. Consequently, panoramic 360° field-of-view (FoW) imaging is becoming an industry prerequisite. However, to obtain a complete view of the area around a vehicle, several sensor systems are necessary. This paper explains how panomorph optics can satisfy the needs of various vision applications with only one sensor or panomorph lens configuration. A panomorph lens is a hemispheric wide-angle anamorphic lens with enhanced resolution in a predefined zone of interest. Because panomorph lenses feature an optimal pixel-per-degree relationship, the resulting vision systems provide ideal area coverage, which in turn reduces and maximizes the processing. Here we present how panomorph technology is one of the most promising ways to integrate many automotive vision applications (processing/viewing) onto one single camera module. For example: a single panomorph sensor on the front of a vehicle could provide all the information necessary for assistance in crash avoidance, lane tracking, early warning alerts, parking aids, road sign and pedestrian detection, as well as various video monitoring views for difficult areas such as blind spots. Keywords: wide-angle lens, panoramic, panomorph, immersive, hemispheric, anamorphic, 360° vision systems, vision sensors, automotive vehicles, field of view, transportation, driver assistance systems, lane tracking, blind spot, pedestrian detection, road sign detection, parking assistance
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".