Inclusive Landmark based Pedestrian Wayfinding via Multi-modal Directions
Bibliographic record
Abstract
Navigational skills are fundamental to travelling from place to place, personal independence and community integration [2]. Current research in pedestrian wayfinding suggests that people vary significantly in their choice of navigation modalities [6, 7, 25]. In addition, pedestrians with learning disabilities find it difficult to recall routes travelled daily and stay oriented while enroute to unknown locations. This paper proposes a wayfinding interface that has 2 components: 1) temporary poly-coated cardboard signage along with imprinted information indicating a specific destination, minutes by foot, directional arrow and a QR code; 2) online interactive website to provide additional contextualized navigation instructions for pedestrians through various modalities. The University of Toronto Scarborough campus (UTSC) is being used as the physical environment to implement and test the proposed wayfinding interface. The QR code tags link the cardboard signage to the online interface and generate streaming of route instructions in the modes of panoramic video, photographs, aerial map, audio or text. The goal of the proposed wayfinding system is to aid UTSC pedestrians - especially those with learning disabilities - to orient themselves and navigate to their destination through multi-modal landmark-based, turn-by-turn directions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".