Driver heterogeneity in willingness to give control to conditional automation
DOI: 10.1016/j.trf.2024.03.013
archive: archived pipeline: cataloged verified
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Summary
This study investigates driver heterogeneity in the willingness to give (WTG) control to conditional automation (SAE Level 3) within a mixed-traffic environment. The research addresses the gap in understanding actual usage behaviors versus stated intentions, focusing on how individual personality traits, specifically locus of control, and environmental factors influence the voluntary transfer of driving tasks. The authors aim to model both within- and across-class unobserved heterogeneity to identify distinct behavioral segments and predict adoption patterns. The experimental design utilized a Virtual Immersive Reality Environment (VIRE), a high-fidelity digital twin simulator, to mitigate hypothetical bias associated with surveys. Data were collected from 68 participants across two waves, resulting in 172 observations. Participants engaged in 16 distinct scenarios, each varying by four controlled variables: weather (clear/rainy), lighting (day/night), traffic congestion (heavy/light), and multi-tasking requirements (presence/absence of non-driving-related tasks). Each scenario was replicated approximately ten times, lasting about five minutes. The study analyzed two dependent variables: a binary choice of whether to give away control and the extent of adoption measured by the proportion of time spent in automated mode. To address heterogeneity, the researchers employed Mixed Logit (MIXL) and Mixed Latent Class (LCML) models for the binary choice, alongside an Ordinal Logit model for adoption levels. The LCML approach segmented the population into "internalizers" (believing they control events) and "externalizers" (believing external factors control events) based on taste heterogeneity identified in the MIXL model. Results indicated that drivers were significantly more likely to relinquish control during nighttime driving or when engaged in non-driving-related tasks. Conversely, rainy weather combined with light congestion discouraged control sharing. Demographic analysis revealed that students and individuals with master’s degrees exhibited higher WTG, while those with over ten years of driving experience showed the lowest rates. Crucially, the study found that internalizers demonstrated greater heterogeneity in their willingness to give control compared to externalizers, challenging the assumption that internalizers uniformly reject automation due to high self-confidence. The findings highlight the importance of accounting for unobserved heterogeneity and personality traits in modeling automated vehicle adoption. By identifying distinct latent segments, the study provides policymakers and manufacturers with insights into target market segments and the specific conditions that foster trust and acceptance. The integration of digital twins and VR offers a robust methodology for capturing realistic behavioral responses, suggesting that future models should incorporate psychological segmentation to accurately predict the voluntary use of conditional automation in mixed-traffic settings.
Key finding
Drivers are more willing to give control to conditional automation during nighttime or when performing non-driving-related tasks, with internalizers showing greater behavioral heterogeneity than externalizers.
Methodology
simulator
Sample size: 68
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 (13 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | canonical_url | — | — | 9 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| enrich | failed | — | — | — | 14 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-05-07 |
| 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.
- acceptance adoption
- automation
- situational awareness
- trust calibration
- automation surprise
- takeover transitions
Information type
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- Theoretical Contribution: computational model, conceptual framework, theory or model