Improving Truck Driver and Vulnerable Road User Interactions Through Driver Training: An Interview Study With Canadian Subject Matter Experts
Bibliographic record
Abstract
Collisions between trucks and vulnerable road users (VRUs) represent one of the most severe types of road collisions. Research initiatives to mitigate truck–VRU collisions include vehicle redesign, infrastructure improvements, and driver warning systems. Truck driver training is a complementary solution that can lead to improved driving behavior and collision reduction. We aimed to understand the perceptions of subject matter experts of current truck driver training in Ontario, Canada, to identify gaps and potential improvements, particularly targeting VRU safety in urban areas. Further, we investigated the degree to which VRU safety is covered and the potential to incorporate it in training through simulators. We conducted semi-structured interviews with 21 truck driver trainees, novice and experienced truck drivers, a driving instructor, and road safety professionals. The participants highlighted a notable gap between current training and real-world truck driving. VRU safety and hazard anticipation training emerged as a missing component in training. Nine out of 11 participants who received simulator training perceived it positively and recommended it for its safe and relatively realistic environment. The main topics that should be incorporated in VRU safety training, as recommended by interviewees, include training on difficult truck maneuvers with presence of VRUs, anticipating hazardous VRU actions, and navigating difficult infrastructure components. This work presents subject matter expert perceptions of current truck driver training in Ontario, Canada, and identifies gaps and improvements targeting VRU safety. Although the study is based on Ontario, the results can apply across Canada and beyond, where trucks pose dangers to VRUs.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".