Hybrid Fuzzy Neural Systems for Real Time Decision Making in Autonomous Vehicles
Bibliographic record
Abstract
The real-time decision making for autonomous vehicles is challenging because the driving environment is high-dimensional, dynamic, and uncertain. One such approach that shows promise is the use of hybrid fuzzy-neural systems which capitalize on the human-like reasoning of fuzzy logic combined with the adaptive learning capabilities of the neural network. In this paper, we will study some of the such systems developed and utilized for improving decision-making in autonomous vehicles. The proposed method employs fuzzy logic to process vague or imprecise data, allowing the system to function in the lack of crisp data or in uncertain situations. At the same time, we have neural networks, which learn from the big data, figure out what is best to do in a variety of situations by gaining experience and improving accuracy for their decisions as time goes on. The hybrid system, by integrating both the model-based and data-driven approaches, is capable of handling complex and dynamic inputs such as variations in traffic, human walking patterns, and sudden obstacles, resulting in more accurate and reliable decision-making in a timely manner. Experimental evaluations show that H-FN AMURs achieve significantly better navigation accuracy and responsiveness than AMURs based merely on fuzzy logic or neural network models. Combining these systems enables adaptive learning and strong decision-making, necessary for living in an unpredictable environment and assuring passenger safety. Through a well-designed framework, this study addresses the question on how intelligent transportation systems can improve the decision-making processes of autonomous vehicles. Further, a final address will explore the potential of integrating driving-by-weight simulations to create efficient hybrid models for such autonomous navigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".