Collision Prevention While Driving in Real Traffic Flow Using Emotional Learning Fuzzy Inference Systems
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper proposes a methodology for collision prevention in car following scenarios. For this purpose, Emotional Learning Fuzzy Inference System (ELFIS) approach is used to simulate and predict the behavior of a driver-vehicle-unit in a short time horizon ahead in the future. Velocity of the follower vehicle and relative distance between the follower and the lead vehicles are predicted in a parallel structure. Performance of the proposed algorithm is assessed using real traffic data and superior accuracy of this method is demonstrated through comparisons with another available technique (ANFIS). The predicted future driving states are then used to judge about safety of the current driving pattern. The algorithm is used to generate a warning message while a safe-distance keeping measure is violated in order to prevent a collision. Satisfactory performance of the proposed method is demonstrated through simulations using real traffic data. The proposed method can be applied, in real time, for a variety of applications including driver assistant and collision prevention systems as well as other intelligent transportation applications.</div></div>
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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 | 0.001 |
| 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".