A multimodal psychological, physiological and behavioural dataset for human emotions in driving tasks

Abstract

Human emotions are integral to daily tasks, and driving is now a typical daily task. Creating a multi-modal human emotion dataset in driving tasks is an essential step in human emotion studies. we conducted three experiments to collect multimodal psychological, physiological and behavioural dataset for human emotions (PPB-Emo). In Experiment I, 27 participants were recruited, the in-depth interview method was employed to explore the driver’s viewpoints on driving scenarios that induce different emotions. For Experiment II, 409 participants were recruited, a questionnaire survey was conducted to obtain driving scenarios information that induces human drivers to produce specific emotions, and the results were used as the basis for selecting video-audio stimulus materials. In Experiment III, 40 participants were recruited, and the psychological data and physiological data, as well as their behavioural data were collected of all participants in 280 times driving tasks. The PPB-Emo dataset will largely support the analysis of human emotion in driving tasks. Moreover, The PPB-Emo dataset will also benefit human emotion research in other daily tasks.

An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios

“…There are several well-known datasets, such as NASS-CDS 15 and GIDAS 16 , which provide detailed information on human driver injuries and are used for studying and evaluating injury patterns and severity. Furthermore, there are also other datasets that focus on the psychological, physiological, and behavioral aspects of human drivers during driving tasks, such as a multi-modal human emotion dataset for driving tasks 17 and a multimodal dataset for various forms of distracted driving 18 . The latter records the heart rates, breathing rates and eye tracking data of human drivers under four different conditions.…”

Cross-subject EEG linear domain adaption based on batch normalization and depthwise convolutional neural network

“…Moreover, this expansion ability of mapping process can also be used in mapping EEG across different research areas. For example, the emotional EEG signals elicited in daily life can be used to recognize driving emotion [47], which is helpful for building driving assistance systems. Additionally, the EEG signals elicited in different scenarios can be intercommunicated and the content of them can be enriched by each other.…”

Intelligent Cockpit for Intelligent Vehicle in Metaverse: A Case Study of Empathetic Auditory Regulation of Human Emotion

“…Although the intelligent cockpit is an important space for the human-vehicle relationship evolving from a driving tool to an intimate partner, the future intelligent cockpit still needs to answer a challenging question, that is how to take advantage of the continuous development of innovative technologies to allow the intelligent cockpit to provide people with precise functionalities and services for the safe, efficient, and enjoyable experience [7], [10]. To address this problem, under the CPSS framework, we propose an intelligent cockpit composition framework, discussing the compositions of the intelligent cockpit and the technologies each composition should have (see the discussion in Section I-A for details).…”

“…Therefore, selected video clips were played to stimulate the participants' anger. From the PPB_Emo dataset [10], we selected three video clips recorded to be used to elicit participants' anger. For example, others keep the high beam on while meeting the car, which affects the vision.…”