A Dataset on Takeover during Distracted L2 Driving

Abstract Automated driving systems enable drivers to perform various non-driving tasks, which has led to concerns regarding driver distraction during automated driving. These concerns have spurred numerous studies investigating driver performance of fallback to driving (i.e., takeover). However, publicly available datasets that present takeover performance data are insufficient. The lack of datasets limits advancements in developing safe automated driving systems. This study introduces TD2D, a dataset collected from 50 drivers with balanced gender representation and diverse age groups in an L2 automated driving simulator. The dataset comprises 500 cases including takeover performance, workload, physiological, and ocular data collected across 10 secondary task conditions: (1) no secondary tasks, (2) three visual tasks, and (3) six auditory tasks. We anticipate that this dataset will contribute significantly to the advancement of automated driving systems.