Why drivers are frustrated: results from a diary study and focus groups

Author:

Bosch EstherORCID,Ihme Klas,Drewitz Uwe,Jipp Meike,Oehl Michael

Abstract

Abstract Introduction Designing 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. Methods Therefore, 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. Results It 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.

Funder

Bundesministerium für Bildung, Wissenschaft und Kultur

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Transportation,Automotive Engineering

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Frustration control during driving using auditory false heart rate feedback;Transportation Research Part F: Traffic Psychology and Behaviour;2024-02

2. Frustration Recognition Using Spatio Temporal Data: A Novel Dataset and GCN Model to Recognize In-Vehicle Frustration;IEEE Transactions on Affective Computing;2023-10-01

3. Fifty shades of frustration: Intra- and interindividual variances in expressing frustration;Transportation Research Part F: Traffic Psychology and Behaviour;2023-04

4. A Study on Parking Space Allocation and Road Edge Detection for Optimizing Road Traffic;Intelligent Cyber Physical Systems and Internet of Things;2023

5. Multimodal Estimation of Frustrative Driving Situations Using a Latent Variable Model;2022 13th IEEE International Conference on Cognitive Infocommunications (CogInfoCom);2022-09-21

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