Building BROOK: A Multi-modal and Facial Video Database for Human-Vehicle Interaction Research

TL;DR

This paper introduces BROOK, a comprehensive multi-modal facial video database designed for advancing human-vehicle interaction research, enabling better understanding of drivers' affective states and driving behaviors.

Contribution

The creation of BROOK, a detailed multi-modal facial video database with physiological and driving data, and a neural network predictor leveraging this database for multi-modal prediction.

Findings

BROOK supports multi-modal data analysis for driver state prediction

Neural network predictor achieves promising results using BROOK data

The database facilitates future research in human-vehicle interaction

Abstract

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.