Building BROOK: A Multi-modal and Facial Video Database for Human-Vehicle Interaction Research
This work provides a foundational resource for researchers studying affective states and driving styles in autonomous vehicles, though it is incremental as it builds on existing database and prediction methods.
The authors tackled the lack of a comprehensive database for human-vehicle interaction research by introducing BROOK, a public multi-modal database with facial video records, and demonstrated its utility with a neural network-based predictor that achieved multi-modal predictions including heart rate, skin conductance, and speed from facial videos.
With the growing popularity of Autonomous Vehicles, more opportunities have bloomed in the context of Human-Vehicle Interactions. However, the lack of comprehensive and concrete database support for such specific use case limits relevant studies in the whole design spaces. In this paper, we present our work-in-progress BROOK, a public multi-modal database with facial video records, which could be used to characterize drivers' affective states and driving styles. We first explain how we over-engineer such database in details, and what we have gained through a ten-month study. Then we showcase a Neural Network-based predictor, leveraging BROOK, which supports multi-modal prediction (including physiological data of heart rate and skin conductance and driving status data of speed)through facial videos. Finally, we discuss related issues when building such a database and our future directions in the context of BROOK. We believe BROOK is an essential building block for future Human-Vehicle Interaction Research.