Post Take-Over Performance Varies in Drivers of Automated and Connected Vehicle Technology in Near-Miss Scenarios
DOI: 10.1177/00187208231219184
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 evidence that partial automation degrades situation awareness and that connected vehicle technology may offer critical hazard warnings, the research aims to determine if drivers instructed to actively monitor the ADS perform better than those instructed to passively monitor it, particularly when hazards are obscured. The experiment utilized a distributed driving simulator platform involving 48 licensed drivers. Participants were randomly assigned to either an "active" condition (hands on wheel, foot over brake, attentive) or a "passive" condition (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 conflict vehicle. A trained experimenter controlled the conflict vehicle in a networked simulator to induce near-misses. Connected vehicle technology provided reliable audiovisual warnings prior to potential collisions. Performance was measured via mean longitudinal velocity, standard deviation of longitudinal velocity, and mean longitudinal acceleration. Statistical analysis employed default Bayesian ANOVA to assess the effects of driving instruction, scenario type, and obstruction presence. Results indicated that drivers in the passive ADS group exhibited significantly higher and more variable deceleration rates compared to the active group, suggesting less stable vehicle control during critical conflicts. Specifically, passive drivers decelerated at a mean rate of 1.49 m/s², whereas active drivers decelerated at 0.86 m/s². Additionally, passive drivers showed greater variability in speed control. In scenarios with visual obstructions, particularly the running stop sign scenario, drivers failed to slow down adequately despite receiving reliable audiovisual warnings, maintaining higher velocities than in obstruction-absent conditions. Trust 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 leads to degraded post-takeover performance, characterized by erratic and aggressive braking, likely due to reduced situation awareness. Crucially, the study demonstrates that connected vehicle warnings alone may be insufficient to mitigate risks in near-miss scenarios where hazards are visually obstructed, as drivers failed to anticipate the danger despite alerts. The authors conclude that designers of automated and connected vehicle technologies must consider alternative cueing strategies, such as different timing or types of warnings, to effectively support driver anticipation and safe navigation in high-risk, obstructed environments.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-08 |
| 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 | 2026-06-08 |
| 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
- automation surprise
- automation
- takeover transitions
- anticipation
- automation complacency bias
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
- Theoretical Contribution: conceptual framework, computational model