Exploring driving anger-caused impairment of takeover performance among professional taxi drivers
DOI: 10.1016/j.aap.2024.107686
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Summary
This study investigates how driving anger affects the takeover performance of professional taxi drivers during partially automated driving (SAE Level 2). While automation is expected to enhance safety, taxi drivers are prone to driving anger due to high competition and time pressure, which may impair their ability to safely resume control of the vehicle. The research specifically examines these effects across three distinct transition scenarios: Mandatory Automation-Initiated Transition (MAIT), Mandatory Driver-Initiated Transition (MDIT), and Optional Driver-Initiated Transition (ODIT). The researchers conducted a mixed-design simulator experiment with 47 professional taxi drivers from Xi’an, China. Participants were randomly assigned to either an anger induction group or a calmness control group. Anger was induced through video clips of reckless driving and personal recall exercises, while the control group viewed nature scenes. The experiment utilized a fixed-based driving simulator equipped with eye-tracking and electrodermal activity (EDA) sensors to measure physiological responses. Drivers encountered three specific scenarios: a sudden motorcycle lane change (MAIT), a passenger order route error requiring manual correction (MDIT), and a vehicle overtaking maneuver where takeover was optional (ODIT). Results indicated that driving anger significantly impaired cognitive and behavioral performance. Physiologically, angry drivers exhibited a narrower field of attention, evidenced by smaller standard deviations in horizontal fixation points, and poorer hazard perception, marked by longer saccade latency and reduced skin conductance response amplitude. Behaviorally, in MAIT and MDIT scenarios, angry drivers demonstrated longer takeover times and inferior vehicle control stability, characterized by higher lateral position deviations. In ODIT scenarios, angry drivers were more likely to deactivate automation and take over aggressively, displaying higher maximal resulting acceleration and a tendency to refuse yielding to other road users. These findings highlight that driving anger poses a specific safety risk for professional drivers using partially automated systems, particularly by narrowing attention and promoting aggressive takeover behaviors. The study underscores the need for safety interventions and system designs that account for emotional states in professional driving contexts. By identifying how anger degrades performance across different transition types, the research provides critical insights for improving the integration of automation in the taxi industry and addressing the unique psychological challenges faced by professional drivers.
Key finding
Driving anger impaired taxi drivers' takeover performance by narrowing visual attention and increasing aggressive behaviors, resulting in longer takeover times and reduced vehicle stability during mandatory transitions.
Methodology
simulator
Sample size: 47
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | partial | scout | — | — | 2 | 2026-05-08 |
| archive | success | unpaywall | — | — | 1 | 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 | semantic_scholar | — | — | 2 | 2026-06-04 |
| 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.
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Information type
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- Empirical Findings: behavioral performance data
- Theoretical Contribution: conceptual framework, theory or model