A Monocular Forward Leading Vehicle Distance Estimation using Mobile Devices
Bibliographic record
Abstract
Keeping the safe distance from the leading vehicle is crucial for transportation companies with a fleet of old cars. While modern Advanced Driver Assistant Systems (ADAS) might be able to estimate the distance from the front-leading vehicle, traditional ADAS do not usually offer this feature. An alternative solution is to monitor the distance using smartphones that are attached to a place such as a sun visor. The basic idea behind this approach is to detect the front-leading vehicle using the smartphone camera and estimate its distance from the car. Although SSD can achieve real-time performance on powerful GPUs, it remains challenging to run this model in real-time on mobile devices. In this paper, we propose a monocular distance estimator for forward-leading vehicles using a smartphone which is faster and more accurate than the state-of-the-art SSD detector. Specifically, we propose a layer-wise method to generate more efficient default boxes for the SSD and develop a lightweight method for estimating the distance accurately. Our experiments show that the proposed method reduces the number of default boxes by an average of 38.4% while it improves the detection rate and the processing speed compared to the original SSD. Moreover, our monocular distance estimator provides a proper safety buffer zone when the distance is greater than 20 meters. A sample video is available at https://youtu.be/-ptvfabBZWA.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".