Training to Support Appropriate Reliance on Advanced Driver Assistance Systems
Bibliographic record
Abstract
Advanced driver assistance systems (ADAS) are becoming more widely available to consumers. While these systems have potential safety benefits, overreliance on ADAS has contributed to several fatal collisions. Training has been identified as one way to address overreliance and support safe use of driving automation. Currently, existing training and related research generally focuses on teaching drivers various system limitations, but there are mixed results regarding the relationship between knowledge of limitations and trust in and reliance on ADAS. Further, this limitation-focused training approach may not be practical (e.g., due to the large number of limitations to learn and remember). This dissertation aims to understand the relationship between knowledge of ADAS limitations, trust, and reliance, and investigate an alternative to limitation-focused training.First, a survey study was conducted and the results suggested that, in addition to impracticalities associated with limitation-focused training, it may not be an ideal approach if targeting a wide range of drivers, as knowledge did not significantly impact trust or reliance intention for drivers who had ADAS experience. Subsequently, training videos were developed to compare limitation-focused training to a novel training approach which highlighted the drivers’ responsibility when using ADAS (i.e., responsibility-focused training). An online study revealed limited differences between the limitation-focused and responsibility-focused approach. Where significant differences were found for quantitative measures, they indicated potential drawbacks of the limitation-focused approach. Further, results of semi-structured interviews with participants suggest that limitation-focused training may have the unintended consequence of decreased interest in using ADAS among drivers without ADAS experience. Further investigation with a simulator study showed that even when an attention monitoring system was implemented to remind participants to keep their eyes on the road, there was a benefit of the responsibility-focused training, but not limitation-focused training, on reliance (e.g., lower rate of long glances to a secondary task and taking over control of the vehicle sooner when a potential conflict occurred). Overall, this dissertation adds to the existing literature on the relationships between ADAS knowledge, trust, and reliance. Further, it expands the limited literature on alternatives to limitation-focused training and provides preliminary support to the responsibility-focused training approach.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".