A Dempster-Shafer Sensor Fusion Approach for Traffic Incident Detection and Localization
Bibliographic record
Abstract
Traffic incident detection and localization is an important application in traffic management systems. The ability to detect and localize traffic incidents enables a timely response to accidents and facilitates effective and efficient traffic flow management. This paper presents a sensor-network based approach for tackling the problem of incident localization. Traffic count sensors, which tend to be an element of the road infrastructure, are used as the source of traffic sensory data. Such sensors come in a variety of types and capabilities, providing the potential for complementary and redundant information gathering. Thus, it is conceivable to fuse such sensory information to achieve insightful and accurate incident detection and localization. In this context, the Dempster-Shafer (DS) theory of evidence is used as the foundation for fusing traffic sensory data. In this paper, a traffic model generator and two traffic-counting sensory systems are employed for acquiring traffic data pertinent to the distribution of cars on a given road segment. Experimental analysis on the performance of the proposed approach is provided.
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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".