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. 2025 May 9:6:1548861.
doi: 10.3389/fnrgo.2025.1548861. eCollection 2025.

Detecting sources of anger in automated driving: driving-related and external factor

Affiliations

Detecting sources of anger in automated driving: driving-related and external factor

Jordan Maillant et al. Front Neuroergon. .

Abstract

Introduction: Anger while driving is often provoked by on-road events like sudden cut-offs but can also arise from external factors, such as rumination of negative thoughts. With the rise of autonomous vehicles, drivers are expected to engage more in non-driving activities, potentially increasing the occurrence of anger stemming from non-driving-related sources. Given the well-established link between anger and aggressive driving behaviors, it is crucial to detect and understand the various origins of anger in autonomous driving contexts to enhance road safety.

Methods: This study investigates whether physiological (cardiac and respiratory activities) and ocular indicators of anger vary depending on its source (driving-related or external) in a simulated autonomous driving environment. Using a combination of autobiographical recall (AR) for external anger induction and driving-related scenarios (DS), 47 participants were exposed to anger and/or neutral conditions across four groups.

Results: The results revealed that combined anger induction (incorporating both external and driving-related sources) led to higher subjective anger ratings, more heart rate variability. However, when examined separately, individual anger sources did not produce significant differences in physiological responses and ocular strategies.

Discussion: These results suggest that the combination of anger-inducing events, rather than the specific source, is more likely to provoke a heightened state of anger. Consequently, future research should employ combined induction methods to effectively elicit anger in experimental settings. Moreover, anger detection systems should focus on the overall interplay of contributing factors rather than distinguishing between individual sources, as it is this cumulative dynamic that more effectively triggers significant anger responses.

Keywords: anger detection; anger sources; automated driving; ocular behavior; physiological indicators; subjective evaluations.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Overview of the driving simulator. Left (A) View of the cabin and screen, Right (B), (1) Position of the eye tracker system, (2) Position of the tactile tablet used to transmit instructions and emotion questionnaires during the experiment.
Figure 2
Figure 2
Time course of the experiment. Subjective evaluations are assessed after the baseline, post AR and post DS. Anger effects in physiological and ocular data are only measured from the last minute of the driving scenario.
Figure 3
Figure 3
Mean delta scores (difference from baseline) of Valence, Arousal and Control dimensional scale at the different moments (Post AR, Post DS) of the experiment and across the four groups. Error bars represent standard errors. Significant pairwise group comparisons (p < 0.05) are reported.
Figure 4
Figure 4
Mean delta scores (difference from baseline) of the Anger emotion scale at the different moments (Post AR, Post DS) of the experiment and across the four groups. Error bars represent standard errors. Significant pairwise group comparisons (p < 0.05) are reported.
Figure 5
Figure 5
Delta scores (rapport from baseline) across groups for HRV_SDNN, HRV_LF and BRV_RMSSD during the last minute of the driving scenarios. Error bars represent standard errors. *p < 0.05; . < 0.10.

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