Open AccessA Multimodal Approach for Monitoring Driving Behavior and Emotions
Author(s) -
Arash Tavakoli,
Vahid Balali,
Arsalan Heydarian
Publication year - 2020
Language(s) - English
Resource type - Reports
DOI - 10.31979/mti.2020.1928
Subject(s) - affect (linguistics) , valence (chemistry) , distraction , computer science , aggressive driving , affective computing , psychology , applied psychology , cognitive psychology , human–computer interaction , human factors and ergonomics , poison control , communication , medicine , physics , environmental health , quantum mechanics
Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways.