Factors That Affect Drivers’ Perception of Closing and an Immediate Hazard
DOI: 10.1177/00187208211009028
archive: archived pipeline: cataloged verified
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Summary
This study investigates how monitoring instructions for Automated Driving Systems (ADS) and the presence of visual obstructions affect driver performance during near-miss scenarios. Motivated by the need to understand how connected vehicle technology can mitigate risks associated with partial automation, the research addresses the gap in knowledge regarding driver takeover responses when hazards are not immediately visible. The authors hypothesized that drivers instructed to actively monitor the ADS would respond more effectively to near-misses than those instructed to passively monitor it, and that visual obstructions would negatively impact hazard anticipation. The experiment utilized a distributed driving simulator platform involving 48 licensed drivers. Participants were randomly assigned to either an "active" condition (instructed to keep hands on the wheel and foot over the brake) or a "passive" condition (instructed to be ready to take over only if requested). Each participant navigated eight scenarios derived from the NHTSA Pre-Crash Scenario Typology, including intersection and highway merging events, with or without visual obstructions (e.g., buses, dumpsters, hedges) blocking the view of a conflicting vehicle. An experimenter manually controlled the conflicting vehicle in a networked simulator to induce near-miss events. The study measured mean longitudinal velocity, standard deviation of longitudinal velocity, and mean longitudinal acceleration from warning onset to the projected collision point. Trust in the ADS was also assessed using established questionnaires. Statistical analysis employed default Bayesian ANOVA to evaluate the effects of driving instruction, scenario type, and obstruction presence. The results indicated that drivers in the passive ADS group exhibited significantly higher and more variable deceleration rates compared to those in the active group, suggesting poorer vehicle control during critical conflicts. Specifically, passive drivers showed a mean deceleration rate of approximately 1.49 m/s², which, while below the threshold for hard braking, was higher than the active group’s rate of 0.86 m/s². Additionally, passive drivers demonstrated greater variability in longitudinal velocity, indicating less stable speed control. In scenarios with visual obstructions, particularly the running stop sign scenario, drivers failed to slow down adequately despite receiving reliable audiovisual warnings from the connected vehicle system. Trust levels in the ADS did not significantly change between the beginning and end of the experiment for either group. The findings suggest that passive monitoring of ADS degrades driver situation awareness, leading to increased and more erratic deceleration during near-miss events, even when connected vehicle warnings are present. The study concludes that reliable audiovisual alerts may be insufficient for hazards obscured by environmental factors, as drivers may fail to anticipate the danger correctly. These results imply that designers of automated and connected vehicle technologies must consider alternative timing and types of cues to effectively inform drivers of imminent hazards in high-risk, obstruction-heavy scenarios. Future research should further explore the interactive effects of automation and connectivity on hazard anticipation and safe navigation.
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-07 |
| archive | success | canonical_url | — | — | 7 | 2026-06-09 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-09 |
| chunk | success | chunk | — | — | 1 | 2026-06-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-09 |
| promote | success | — | — | — | 1 | 2026-06-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 8 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- situational awareness
- hazard perception
- automation surprise
- anticipation
- rail grade crossings
- crash reconstruction hf
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: behavioral performance data
- Methodological Resource: tool software
- Theoretical Contribution: theory or model