Bridging Connected Vehicles with Artificial Intelligence for Smart First Responder Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Citizen-centric methods leverage connectivity and Artificial Intelligence (AI) methodologies to improve smart city services. Among these services, intelligent re-design of first responder services call for novel solutions that save city resources and result in faster response time. To this end, we propose to utilize sensory data in connected vehicles to capture contextual details that can be obtained through various Convolutional Neural Network (CNN) architectures to determine which set of first responders should be called in the case of an accident. We use real images from a rich dataset of accidents involving different types of vehicles to train and test the CNNs. Through experimental results, we show that when ResNet-34 network is augmented with one fit cycle, image augmentation and hidden layer unfreezing methods can result in 88.9% accuracy in the prediction of the required first responder(s) in case of an accident solely based on the captured images.
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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 | 0.001 | 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) | 0.001 | 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 it