论文标题
BROOK数据集：用于利用人力车交互式设计的数据驱动技术的操场
BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in Human-Vehicle Interactive Designs
论文作者
论文摘要
新兴的自动驾驶汽车（AV）造成了巨大的潜力，可以利用数据驱动的技术来进行自适应和个性化的人车相互作用。但是，缺乏高质量和丰富的数据支持限制了探索数据驱动技术的设计空间并验证混凝土机制的有效性的机会。我们的目标是初始化为探索数据驱动的人车相互作用设计的构建基础的努力。为此，我们介绍了带有面部视频记录的多模式数据集Brook Dataset。我们首先简要介绍了构建Brook数据集的理由。然后，我们阐述了如何通过一年的研究来构建当前版本的Brook数据集，并概述数据集。接下来，我们提出了三个使用Brook来证明BROOK数据集的适用性的示例研究。我们还可以从构建Brook数据集中确定关键的学习课程，并讨论Brook数据集如何培养大量的后续研究。
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.
