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.
Advances in technologies, such as intelligent connected vehicles and the metaverse are driving the rapid development of automotive intelligent cockpits. From the perspective of the cyber-physical-social system (CPSS), this study proposed the intelligent cockpit composition framework which includes three layers of perception, cognition and decision, and interaction. Meanwhile, we also describe the relationship between the intelligent cockpit framework and the outside environment. The framework can dynamically perceive and understand humans, and provide feedback on the understanding results, which is beneficial to provide a safe, efficient, and enjoyable experience for humans in the intelligent cockpit. In the cognition and decision layers of the proposed framework, we design a case study of active empathetic auditory regulation of driver anger, focusing on improving road traffic safety. We conducted an in-depth interview experiment and designed two auditory regulation materials of active empathy speech and text-to-speech (TTS) speech. Next, 30 participants were recruited, and they completed a total of 240 anger-regulated driving experiments in the straight and obstacle avoidance scenarios. Finally, we quantitatively analyzed and compared the participants' subjective feelings, physiological changes, driving behaviors, and driving risks, as well as validated the driver anger regulation quality of AES and TTS. The proposed research methods results are beneficial to the design of future intelligent cockpit emotion regulation systems, toward a better intelligent cockpit.
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