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

Junyu Liu, Yicun Duan, Zhuoran Bi, Xiaoxing Ming, Wangkai Jin, Zilin Song, Xiangjun Peng

Abstract: Emerging Autonomous Vehicles (AV) breed great potential 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 on how to build the current version of BROOK dataset via a year-long study, and give an overview of the dataset. Next, we present two example studies using BROOK to justify the applicability of BROOK dataset. We also believe BROOK dataset can foster an extensive amount of follow-up studies, and it’s available at https://github.com/Junyu-Liu-Nate/BROOK-dataset.