Psychophysiological responses to takeover requests in conditionally automated driving
archive: archived pipeline: cataloged verified
Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)
Summary
This study investigates psychophysiological responses to takeover requests (TORs) in SAE Level 3 conditionally automated driving, addressing safety concerns regarding drivers’ ability to negotiate control transitions when disengaged from the driving loop. While existing literature focuses on behavioral metrics like reaction time and collision avoidance, this research aims to capture drivers’ internal states—specifically cognitive workload, attention, emotion, and situational awareness—using continuous, non-invasive physiological measures. The study examines how non-driving-related tasks (NDRTs), traffic density, and TOR lead time influence these responses. The experiment utilized a high-fidelity fixed-base driving simulator with 102 participants, each experiencing eight takeover scenarios. A within-subjects design manipulated cognitive load via visual N-back memory tasks (1-back for low load, 2-back for high load), traffic density (light vs. heavy), and TOR lead time (4 seconds vs. 7 seconds). Psychophysiological data, including gaze behavior, heart rate (HR), galvanic skin response (GSR), and facial expressions, were recorded during two stages: the automated driving phase while performing NDRTs and the takeover transition phase. Linear mixed models and chi-squared tests were employed to analyze the effects of independent variables on physiological metrics, with time-series analysis conducted for significant findings. During the automated driving stage, high cognitive load resulted in lower heart rate variability, narrower horizontal gaze dispersion, and reduced eyes-on-road time compared to low cognitive load, indicating increased mental effort and reduced situational awareness. During the takeover transition, a 4-second lead time caused inhibited blink numbers and higher maximum and mean GSR phasic activation compared to a 7-second lead time, suggesting heightened attention and stress under tighter time constraints. Heavy traffic density led to significantly more heart rate acceleration patterns than light traffic, interpreted as a defensive response to environmental rejection. Additionally, GSR phasic activation and blink suppression were negatively correlated with emotional valence, linking higher physiological arousal to more negative emotional states. The findings demonstrate that psychophysiological measures provide sensitive, real-time indicators of drivers’ internal states during automated driving transitions, complementing traditional behavioral metrics. The study concludes that these measures can effectively monitor workload, attention, and emotion, offering valuable insights for the development of driver monitoring systems and adaptive alert mechanisms in future automated vehicles.
Key finding
Heart-rate variability, gaze dispersion, GSR, and blink measures tracked cognitive load and takeover-request lead-time/traffic-density manipulations, supporting psychophysiological monitoring as a complement to behavioral takeover metrics.
Methodology
simulator
Sample size: N=102
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 discover_arxiv on 2026-05-04 (5 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 4 | 2026-05-28 |
| archive | success | — | — | — | 1 | 2026-05-04 |
| 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 | success | — | — | — | 1 | 2026-05-04 |
| promote | success | — | — | — | 1 | 2026-05-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 17 | 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.
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: physiological data, behavioral performance data
- Methodological Resource: measurement protocol