Impact of anger-inducing situations on driver takeover behavior in highly automated vehicles
Year: 2025
Type: article
Study design: Clinical Trial
Abstract: Motor vehicle accidents, often caused by human error, remain a significant concern. While automated vehicles have the potential to reduce these accidents by handling driving tasks, unnecessary human takeovers, especially when the automated system is operational, can reintroduce error. This study investigates how situational factors known to trigger anger influence takeover behavior and emotional responses in levels 4 and 5 automated vehicles. Using a driving simulator, 60 participants were randomly assigned to either a goal-aligned condition (clear weather, on-time departure, no traffic) or a goal-conflicting condition (dense fog, delayed departure, slow vehicles). Participants could freely choose between manual and automated driving modes. Results showed a significant increase in takeover frequency, higher negative affect and anger in the goal-conflicting condition compared to the goal-aligned condition. Qualitative data gathered from open-ended questions revealed increased stress and frustration leading to more frequent manual takeovers in goal-conflicting conditions, while participants felt calmer with fewer takeovers in goal-aligned conditions. No link was found between takeover behavior and trust in driving automation. These findings highlight the importance of designing Automated Driving Systems (ADS) that minimize stressors and consider drivers' emotional states to enhance safety and comfort. In this regard, incorporating real-time emotional monitoring and employing cognitive behavioral therapy (CBT) strategies (e.g., situation reappraisal) may help mitigate driver emotional states and prevent unnecessary takeovers.
Source: Accident Analysis & Prevention
Authors: Robin Cazes, Valérie Camps, Céline Lemercier
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,150
OpenAlex ID: W4409536339
