A Real-time Vehicle–Pedestrian Collision Avoidance System Exploiting Lightweight Smartphone App
Bibliographic record
Abstract
Road accidents are the leading cause of death, resulting in thousands of deaths and major financial suffering in our society. Potential collisions between automobiles and pedestrians should be detected prior to their occurrence in order to offer early warnings. In recent years, numerous solutions have been presented to avoid vehicle-pedestrian accidents. However, the majority of these systems need substantial infrastructure, which is costly, difficult to implement on a large scale and incurs heavy maintenance. We propose a collision avoidance system that utilizes smartphones and requires no additional hardware resources. Our proposed system includes a lightweight app that generates trajectories as a prediction of future locations on the user’s side and sends it to the cloud. The trajectory updates are processed on the cloud for finding potential collisions and sending alerts to the possibly colliding devices in advance. The smartphone app is power consumption-wise less expensive as it requires only location updates and does not intervene with any other sensor. The real road experiments show impressive accuracy in generating timely, relevant warnings.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".