SafePath: Exploiting Ubiquitous Smartphones to Avoid Vehicle–Pedestrian Collision
Bibliographic record
Abstract
Every year, over 4700 traffic fatalities and 75000 crash injuries involve pedestrians in the United States. Effective solutions are urgently needed to prevent vehicle–pedestrian collision accidents. Many driving assistance systems are proposed to address this problem; however, they require additional infrastructures that may result in higher costs and be difficult to deploy on a large scale. In this article, we propose SafePath, which uses the ubiquitous smartphones to avoid vehicle–pedestrian collision. Specifically, SafePath utilizes the smartphones to broadcast the redesigned service set identifier (SSID) messages containing users’ information (e.g., location, direction, etc.) and scan the surroundings via wireless communications. Considering the limited communication range and the possible interference, and obstruction of obstacles, we propose a collaborative mechanism to enhance the transmission capability, hence predicting the collisions in advance effectively. We also design a risk evaluation scheme to calculate the probability of accidents and inform users to take actions against accidents at different levels. We implement SafePath on the Android platform and conduct extensive real-road experiments to evaluate the system performance. The experimental results demonstrate that SafePath can provide twice the transmission range compared with other collision-avoiding systems. Moreover, it also can significantly reduce the probability of vehicle–pedestrian collisions by up to 81.4%, with respect to other compared collision-avoiding systems in our real-road test.
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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 | 0.001 | 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 | 0.001 |
| 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".