Systematic analysis of fatal collisions between mobile equipment and pedestrian workers to inform the use of proximity detection devices
Bibliographic record
Abstract
Context. Collisions between mobile industrial equipment and pedestrian workers (PWs) are a major occupational health and safety concern, particularly on construction sites (e.g., during earth-moving operations). The use of driver aids, such as proximity warning devices designed to alert workers and drivers of potential collisions, is a growing and constantly developing avenue of prevention (e.g., AI-enhanced cameras; RFID tags). However, structured feedback to guide the implementation of these technologies on mobile equipment remains rare (e.g., technology choice, installation and settings). Objective/Methodology. In this context, a systematic analysis of all fatal accident reports related to this issue for the province of Quebec (Canada) over the period 2013-2023 was carried out to provide insight into field constraints (n=34). A database was developed through a detailed review of these reports, capturing variables such as time, location, established causes, equipment type, PW status, work activities, regulatory compliance, equipment movement, PW position, and the awareness of both the driver and PW just before the accident. Results. The results outline all the circumstances of these accidents, as described in the methodology. For example, the driver was unable to see the PW in 76% of cases, and the PW was unaware of the mobile equipment in 38% of cases. The underlying reasons for these accidents were also analysed. Discussion. In 88% of cases, a PW proximity detection and warning device could potentially have prevented the accident. However, analysis of the reports also highlighted challenges in the implementation of these devices, such as 1) ensuring coverage of the entire danger zone (motion-, steering- and speed-dependent), 2) managing environmental constraints, 3) maintaining the effectiveness of warnings in situations where cohabitation is intended, or 4) accounting for subcontracting and open worksite conditions that complicate the use of certain technologies (e.g., requiring the wearing of a tag).
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.029 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".