Fuzzy Logic in VANET context aware Congested Road and Automatic Crash Notification
Bibliographic record
Abstract
For VANET safety services a context aware system for the Automatic Crash Notification (ACN) is developed while the context aware Congested Road Notification system (CRN) is developed for the convenience services. A simple fuzzy logic model is proposed and compared to different severity estimation models deployed for both systems. The performance of the ACN models is compared using a test collection that is based on nineteen years of real life crash records associated with their severity levels while the performance of the CRN models is tested using nearly 500,000 different urban and rural freeways flow situations associated with their congestion severity levels. The non-binary Spearman correlation coefficient and the Average Distance Measure (ADM) are used to evaluate the performance of the tested models. Results show that the simple fuzzy severity estimation model has a comparable performance to more complicated systems such as the CoTEC (CoOperative Traffic congestion detECtion) fuzzy system and the URGENCY algorithm, and outperforms the binary severity estimation models for the ACN and CRN systems.
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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 | 0.000 | 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 | 0.000 |
| 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 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".