Manipulating Optical Looming to Influence Perception of Time-To-Collision and its Application in Automobile Driving
Bibliographic record
Abstract
Direct manipulation of optical looming provides convincing evidence of the contribution of optical looming in estimating time-to-collision (TTC). Precise manipulation of optical looming cues can be easily accomplished through computational scaling of (real or virtual) objects without impinging upon the naturalness of the task. This paper first discusses three ways to manipulate optical looming by scaling object size in order to influence perception of TTC. Then two principles affecting the implementation of optical looming manipulation are addressed. Next by revisiting the data of previous research, influences of knowledge about the optical looming manipulation and practice on the effect of optical looming manipulation are discussed. This supports our proposed principles and confirms the possibility of introducing the concept of optical looming manipulation into actual automobile design. Finally a potential application of optical looming manipulation, a dynamic brake light system, is proposed for automobile driving, to reduce the frequency of rear-end collisions. Issues in implementation of such a system and future research to validate it are also identified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".