A Link Between Trust in Technology and Glance Allocation in On-Road Driving
DOI: 10.17077/drivingassessment.1645
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Summary
This study investigates the relationship between drivers’ pre-exposure trust in technology and their subsequent visual glance behavior while interacting with novel in-vehicle voice interfaces. Motivated by the understanding that trust influences reliance on automation and engagement levels, the research aims to determine if self-reported trust correlates with specific eye-movement patterns during on-road driving. The authors hypothesize that trust in general technology and new car technologies specifically would modulate the frequency and duration of glances directed at the device versus the roadway, potentially impacting safety and task efficiency. The analysis utilized secondary data from a large-scale on-road study involving 80 participants equally distributed across age groups and genders. Participants were randomly assigned to drive either a 2013 Chevrolet Equinox or a 2013 Volvo XC60, each equipped with distinct voice-based navigation systems. Before driving, participants rated their trust on a 10-point scale across three categories: general technology, established car technologies, and new car technologies. While driving on a divided interstate, participants performed voice-based address entry tasks. The Chevrolet system required continuous single-string entry, whereas the Volvo system used a step-by-step approach with confirmations. Researchers manually coded eye glances from video recordings, analyzing metrics such as total glances per minute, glances to the device per minute, and long-duration glances (>2 seconds) to the device. Results indicated that pre-exposure trust in new car technology was significantly positively correlated with the total number of glances per minute across all coded regions for both vehicle groups. However, distinct patterns emerged based on the interface design. In the Volvo group, higher trust in new technology was associated with a higher frequency of glances to the device but fewer long-duration glances (>2s), suggesting a strategy of frequent, brief information sampling. In contrast, no significant relationship between trust and device-specific glances was found for the Chevrolet group. Additionally, lower general trust in technology was significantly associated with longer task completion times in the Chevrolet system, likely due to increased hesitancy in a system lacking step-by-step guidance. Trust in established technologies showed no significant associations with any eye metrics. The findings suggest that trust in technology influences visual scanning strategies, particularly when interacting with novel interfaces. The divergence between the two vehicle groups highlights the role of system design and reliability; the Volvo’s structured, error-tolerant design may have preserved trust and facilitated a specific glance pattern, while the Chevrolet’s error-prone, all-or-nothing entry method may have disrupted the trust-behavior link. These results imply that trust calibration is a critical factor in human-machine interface design. Understanding how trust affects glance allocation can help engineers optimize systems to minimize dangerous off-road glances and improve overall driving safety. Further research is recommended to establish causal relationships and explore the utility of eye metrics as indicators of user trust.
Key finding
Higher pre-exposure trust in new car technology was significantly positively correlated with a higher frequency of glances across all coded regions during voice-based navigation tasks.
Methodology
on_road
Sample size: 80
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 2026-06-04 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: behavioral performance data, observational prevalence
- Methodological Resource: measurement protocol