Abstract
IntroductionDesigning emotion-aware systems has become a manageable aim through recent developments in computer vision and machine learning. In the context of driver behaviour, especially negative emotions like frustration have shifted into the focus of major car manufacturers. Recognition and mitigation of the same could lead to safer roads in manual and more comfort in automated driving. While frustration recognition and also general mitigation methods have been previously researched, the knowledge of reasons for frustration is necessary to offer targeted solutions for frustration mitigation. However, up to the present day, systematic investigations about reasons for frustration behind the wheel are lacking.MethodsTherefore, in this work a combination of diary study and user focus groups was employed to shed light on reasons why humans become frustrated during driving. In addition, participants of the focus groups were asked for their usual coping methods with frustrating situations.ResultsIt was revealed that the main reasons for frustration in driving are related to traffic, in-car reasons, self-inflicted causes, and weather. Coping strategies that drivers use in everyday life include cursing, distraction by media and thinking about something else, amongst others. This knowledge will help to design a frustration-aware system that monitors the driver’s environment according to the spectrum of frustration causes found in the research presented here.
Highlights
Designing emotion-aware systems has become a manageable aim through recent developments in computer vision and machine learning
In order to study this, the two complementary methods of a diary study and focus groups were employed
For situations that occurred during the week of data acquisition, most situations were sorted into the category ‘traffic’ (54.5%), which consisted of the subcategories dense traffic (31.3%), red lights (9%), finding parking (7.5%), construction sites (4.5%), unnecessary traffic rules (1.5%) and unclear traffic management (0.7%)
Summary
Designing emotion-aware systems has become a manageable aim through recent developments in computer vision and machine learning. We see empty roads, happy faces, relaxed people, and children enjoying their rides When comparing this to what we experience every day on the road, reality looks different: We get up too late, rush to work, and get annoyed about slow vehicles in front of us while crying children on the back seat take our last hope of a relaxed start into the day. This is one out of endless examples that may result in an emotion that can crucially influence driver’s focus of attention and wellbeing: frustration. In the focus group study, we investigated user’s daily coping strategies with frustrating situations on the road
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