An Internet-of-Vehicles Powered Defensive Driving Warning Approach for Traffic Safety
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
As a major type of driver assistance technologies, automated warning systems provide drivers and vulnerable road users with safety. These systems, such as forward collision warnings, can detect potential risks nearby and alert the drivers. One shortcoming of such warning systems is that their effectiveness and capability depend on the information collected from sensors existing in a single vehicle, which can be highly limited in the presence of occlusion, leading to irreversible consequences. To overcome this shortcoming, in this paper, we benefit from the vehicular sensing and communication technologies to propose a novel Internet-of-vehicles (IoV) powered framework for defensive driving warning, in which a vehicle can take advantage of other vehicles sensing data through V2V communications. We further evaluate the introduced framework in cyclist protection system scenarios. Simulation results demonstrate how the proposed IoV-based framework can improve warning systems by providing increased situational awareness.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it