BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in Human-Vehicle Interactive Designs

TL;DR

The paper introduces the BROOK dataset, a multi-modal facial video dataset designed to support data-driven human-vehicle interaction research for autonomous vehicles, enabling new exploration and validation of interaction mechanisms.

Contribution

This work provides the first comprehensive multi-modal facial video dataset specifically for human-vehicle interaction, along with insights on dataset creation and potential research applications.

Findings

BROOK dataset enables new research in human-vehicle interaction.

Three example studies demonstrate the dataset's applicability.

Key lessons learned inform future dataset development.

Abstract

Emerging Autonomous Vehicles (AV) breed great potentials to exploit data-driven techniques for adaptive and personalized Human-Vehicle Interactions. However, the lack of high-quality and rich data supports limits the opportunities to explore the design space of data-driven techniques, and validate the effectiveness of concrete mechanisms. Our goal is to initialize the efforts to deliver the building block for exploring data-driven Human-Vehicle Interaction designs. To this end, we present BROOK dataset, a multi-modal dataset with facial video records. We first brief our rationales to build BROOK dataset. Then, we elaborate how to build the current version of BROOK dataset via a year-long study, and give an overview of the dataset. Next, we present three example studies using BROOK to justify the applicability of BROOK dataset. We also identify key learning lessons from building BROOK dataset, and discuss about how BROOK dataset can foster an extensive amount of follow-up studies.