Partial-autonomous frenzy: Driving a level-2 vehicle on the open road
DOI: 10.1007/978-3-319-58475-1_25
archive: archived pipeline: cataloged verified
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Summary
This study addresses the limitation of prior research on autonomous vehicles, which often relies on simulations or surveys rather than direct user experience. The authors investigate how naïve drivers interact with Level-2 partial-autonomous systems—specifically Adaptive Cruise Control (ACC) and Lane Keeping Assist System (LKAS)—during real-world driving. The goal is to assess changes in trust, acceptance, and stress levels after direct exposure, providing insights for designing human-machine interfaces for future highly autonomous vehicles. The experimental design involved ten participants with no prior experience using ACC or LKAS. Participants drove a 2016 Honda Accord equipped with Honda Sensing on open roads, including a 40-minute highway segment. The study utilized a pre-post design, collecting subjective ratings via 10-point Likert scales before and after the drive. During the drive, participants engaged both systems while following a lead vehicle, and their verbal comments were recorded using the thinking-aloud technique. These comments were subsequently analyzed using sentiment analysis to determine affective states toward each system. Results indicated divergent outcomes for the two systems. For ACC, participants reported significantly higher trust and ease-of-use in the post-drive questionnaire compared to pre-drive ratings. Sentiment analysis also yielded a more positive index for ACC. In contrast, ratings for LKAS showed a significant increase in perceived stress over time, with no significant changes in trust or acceptance. Sentiment analysis revealed a lower positive index for LKAS. Qualitative comments highlighted that participants found ACC comfortable and safe, whereas LKAS caused anxiety, particularly in denser traffic or when the system’s lane positioning differed from the driver’s preferred style. Participants expressed skepticism about trusting LKAS at highway speeds and noted that monitoring the system was more challenging than using ACC. The findings suggest that while users may quickly adapt to and trust longitudinal control systems like ACC, lateral control systems like LKAS can induce stress and distrust, especially when the automation’s behavior conflicts with the driver’s habits. The authors attribute the higher acceptance of ACC to participants’ prior familiarity with standard cruise control, whereas LKAS was largely unfamiliar. These results imply that future human-machine interfaces for autonomous vehicles must address the specific challenges of lateral assistance to reduce user stress and improve trust, potentially through adaptive systems that align more closely with individual driving styles.
Key finding
On-road exposure to Level-2 automation produced rising stress ratings (especially for Lane Keeping Assist) and a preference for traffic-free conditions, suggesting Level-2 HMI design needs to better support continuous supervisory monitoring.
Methodology
on_road
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 tag_papers on 2026-05-30.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-06 |
| archive | success | core_acuk | — | — | 7 | 2026-06-04 |
| 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 | — | — | — | 3 | 2026-07-02 |
| promote | success | — | — | — | 2 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 16 | 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.
- automation
- acceptance adoption
- trust calibration
- automation surprise
- situational awareness
- driverless ads
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
- Methodological Resource: tool software
- Theoretical Contribution: conceptual framework