  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.
