Comfort and Safety in Conditional Automated Driving in Dependence on Personal Driving Behavior
DOI: 10.1109/ojits.2023.3323431
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Summary
This study investigates user preferences for conditional automated driving (CAD) behavior, specifically examining how personal driving habits influence perceptions of comfort and safety. As vehicles transition to Level 3 automation, drivers become passengers, raising questions about whether users prefer automated systems to mimic their own driving styles or adopt different behaviors. The research focuses on two frequent motorway scenarios: steady-state car-following and decelerating to a lead vehicle. The authors aim to determine if comfort and safety ratings depend on the discrepancy between personal and automated driving behaviors and whether users can manually demonstrate their desired driving behavior for an automated system. The methodology utilizes data from a real-world study involving 42 participants on a 71-kilometer German motorway route. The study design consisted of two phases. First, participants manually drove the route to record their personal driving behavior (PDB) and, in a subsequent section, demonstrated their desired driving behavior (DDB) for an automated vehicle. Second, participants rode as passengers in an automated vehicle (presented as L3 with a safety driver) while rating the system’s comfort and safety on a seven-point Likert scale. The researchers extracted specific driving situations from vehicle kinematics and surrounding object data, excluding roadworks and curves to ensure comparability. For car-following, time gaps were analyzed; for deceleration, maximum deceleration intensity and timing were evaluated. The study compared PDB and DDB against the automated driving behavior (ADB) to assess correlations with subjective ratings. The results indicate a significant dependency between the differences in personal and automated driving behavior and subjective comfort and safety ratings. Participants generally preferred an automated driving style that was similar to or more defensive than their own personal driving behavior. Specifically, in car-following and deceleration scenarios, users rated the automated system more favorably when it maintained greater distances or decelerated earlier and more softly than they would have manually. Furthermore, the study found that participants were capable of manually demonstrating their desired comfort-oriented driving behavior in both analyzed situations. This demonstrated behavior served as a valid reference for what users expect from an automated system, confirming that DDB-like automated driving was among the preferred behaviors regarding comfort and safety. The significance of these findings lies in their implications for the design of future conditional automated driving systems. The study suggests that a "one-size-fits-all" approach to automated driving behavior may not optimize user acceptance. Instead, systems should ideally adopt a defensive driving style that exceeds the safety margins of the average human driver, particularly in longitudinal control tasks like car-following and braking. Additionally, the ability of users to demonstrate desired behaviors supports the potential for personalized CAD systems that adapt to individual preferences. By aligning automated behavior with user expectations for safety and comfort, manufacturers can enhance trust and acceptance of Level 3 automation, facilitating a smoother transition from active driving to passive passenger roles.
Key finding
Participants prefer a conditional automated driving behavior that is similar to or more defensive than their own personal driving behavior, and they are capable of manually demonstrating this desired behavior.
Methodology
field_study
Sample size: 42
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 2026-05-27.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-27 |
| archive | success | unpaywall | — | — | 2 | 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 | 2026-05-27 |
| 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.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- passenger motion sickness comfort
- acceptance adoption
- following distance
- automation surprise
- speed choice
- eco driving
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, observational prevalence
- Theoretical Contribution: computational model