A Situation-Aware Adaptation Framework for Intelligent Transportation Systems
Bibliographic record
Abstract
A transportation system is usually well-defined and operates based on a specific model defined during system design. However, the system can interact with different objects from its environment at runtime and needs to guarantee its functional and timing behavior even in the presence of adverse or failure situations through self-adaptation. Traditionally, techniques such as design analysis and testing are performed during the system design and development stage. During the adaptation process, the transportation system needs to provide assurance such that it is safe and schedulable. We present an adaptation framework, which guarantees the functional and timing behavior of the system in different situations by creating a knowledge base from mining the video stream of the monitored environment. The knowledge base provides information on system interactions with external objects, constraints imposed on the system due to interactions, and characterizes the runtime behavior. We guarantee the timing behavior by evaluating the constraints and their effects on the performance of the system. The experimental analysis of our work demonstrates that the situation-aware adaptation framework can significantly improve system performance by reducing scheduling overload and response time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".