In-Vehicle Displays for Supporting Operation of Driving Automation Systems: Design and Evaluation using Driver Gaze Measures
Bibliographic record
Abstract
In-vehicle displays can support the safe operation of driving automation systems, but a challenge lies in balancing the information conveyed against situational demands. Driver gaze measures are a useful tool for evaluating such displays as they can provide a proxy for driver attention, particularly when the driver is not physically controlling the vehicle.The objective of this dissertation is to systematically identify the design space for displays aimed at supporting safe operation of automation and provide a comprehensive review of their evaluation using gaze measures. First, a scoping literature review revealed extensive research focus on takeover requests, with relatively less focus on informational displays that communicate the automation’s intent or explicitly identify hazards. Surprisingly, there was little focus on displays that monitor and manage driver attention, which are becoming increasingly available and mandated in some jurisdictions. The gaze measures adopted for evaluation mostly relied on static areas of interest that were not dependent on traffic context. Two subsequent experiments were conducted to evaluate informational and attention management displays in different contexts. A remote face-to-face study was conducted to evaluate three display concepts in the context of intersection incursions, which pose high crash risks. The Combined Display, providing information on both the automation’s intended action and identifying upcoming hazards, outperformed the other two displays (which provided intent and hazard information separately) in terms of trust and reliance, but its usability was rated the lowest by participants, indicating a preference for more concise information. A driving simulator study was conducted to evaluate adapted versions of two of these displays (Combined Display and Automation Intent Display) using driver gaze and vehicular measures. The displays were compared to a baseline attention reminder system. The Combined Display may have diverted attention away from cues in the environment indicating potential traffic conflicts, while Automation Intent information resulted in longer glances at cues. Neither of the displays supported better anticipation of conflicts or reduced engagement with secondary tasks compared to the baseline attention reminder display. Overall, this dissertation synthesizes and adds to the literature on in-vehicle display design and evaluation, with implications for supporting safe operation of driving automation.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".