Investigating explanations in conditional and highly automated driving: The effects of situation awareness and modality
DOI: 10.1016/j.trf.2022.07.010
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Summary
This study addresses the challenge of maintaining driver situation awareness (SA) and trust in conditional and highly automated vehicles (SAE Levels 3 and 4), where drivers often become passive passengers. The authors argue that the "black-box" nature of automated driving systems leads to low SA and trust issues, particularly during unexpected scenarios requiring driver intervention. To mitigate this, the paper proposes a theoretical explanation framework based on Endsley’s three levels of SA: perception (Level 1), comprehension (Level 2), and projection (Level 3). The research investigates how explanations mapped to these SA levels, delivered via different modalities, affect drivers’ situational trust, cognitive workload, and explanation satisfaction. The researchers conducted a between-subjects experiment with 340 participants recruited from Amazon Mechanical Turk. The study utilized a 3 (SA levels: L1, L2, L3) by 2 (modality: visual-only, visual + auditory) factorial design. Participants viewed six simulated driving scenarios depicting unexpected AV behaviors, such as abrupt stops or lane changes. Explanations were provided before the AV’s actions to align with best practices for transparency. SA was measured using the Situation Awareness Global Assessment Technique (SAGAT), while situational trust, explanation satisfaction, and mental workload were assessed using standardized scales (STS-AD, Hoffman’s scale, and DALI, respectively). A control group received no explanations. The results demonstrated that SA-based explanations significantly improved participants’ situation awareness compared to the control condition. Regarding situational trust, Level 2 explanations (comprehension) yielded the highest trust scores, significantly outperforming Level 1 (perception) and Level 3 (projection). However, Level 2 explanations also resulted in higher cognitive workload, likely because participants actively interpreted the information to understand the AV’s reasoning. In terms of explanation satisfaction, participants preferred visual-only explanations for Levels 1 and 2 but favored the combined visual and auditory modality for Level 3, which contained more complex information. This suggests that multimodal delivery helps manage the cognitive load associated with higher-level, projection-based explanations. The findings imply that effective human-AV interaction requires explanations tailored to specific SA levels rather than generic feedback. Level 2 explanations are optimal for building trust by helping drivers comprehend the current situation, though they demand more cognitive effort. The study highlights that modality preferences depend on the complexity of the information; simpler explanations benefit from visual-only delivery, while complex, future-projecting explanations benefit from multimodal support. These insights provide a structured approach for designing explainable AI systems in automated vehicles, aiming to keep drivers "in the loop" and improve acceptance without overwhelming them with unnecessary information.
Key finding
SA Level 2 explanations produced the highest situational trust among drivers, despite incurring greater cognitive workload than other explanation levels.
Methodology
lab_experiment
Sample size: 340
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 openalex_abstract on 2026-05-08 (14 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 3 | 2026-05-29 |
| archive | success | — | — | — | 1 | 2026-05-08 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | failed | — | — | — | 11 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-05-05 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 18 | 2026-06-11 |
| verify | success | — | — | — | 2 | 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
- mode awareness
- automation
- automation surprise
- trust calibration
- situation awareness theory
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: self report data
- Theoretical Contribution: theory or model, conceptual framework