This paper addresses the problem of sharing drivers' face videos for
transportation research while adhering to proper ethical guidelines. The paper
first gives an overview of the multitude of problems associated with sharing
such data and then proposes a framework on how artificial intelligence-based
techniques, specifically face swapping, can be used for de-identifying drivers'
faces. Through extensive experimentation with an Oak Ridge National Laboratory
(ORNL) dataset, we demonstrate the effectiveness of face-swapping algorithms in
preserving essential attributes related to human factors research, including
eye movements, head movements, and mouth movements. The efficacy of the
framework was also tested on various naturalistic driving study data collected
at the Virginia Tech Transportation Institute. The results achieved through the
proposed techniques were evaluated qualitatively and quantitatively using
various metrics. Finally, we discuss possible measures for sharing the
de-identified videos with the greater research community.

本文提出了一种基于人工智能的面部替换技术用于去匿名化驾驶员面部数据来解决交通研究数据共享中的隐私问题，并通过实验证明了这种技术在保护驾驶员个人隐私数据和人类行为研究方面的有效性和前景，最终讨论了可能的共享措施。