  With the advancement of deep learning technology, data-driven methods are
increasingly used in the decision-making of autonomous driving, and the quality
of datasets greatly influenced the model performance. Although current datasets
have made significant progress in the collection of vehicle and environment
data, emphasis on human-end data including the driver states and human
evaluation is not sufficient. In addition, existing datasets consist mostly of
simple scenarios such as car following, resulting in low interaction levels. In
this paper, we introduce the Driver to Evaluation dataset (D2E), an autonomous
decision-making dataset that contains data on driver states, vehicle states,
environmental situations, and evaluation scores from human reviewers, covering
a comprehensive process of vehicle decision-making. Apart from regular agents
and surrounding environment information, we not only collect driver factor data
including first-person view videos, physiological signals, and eye attention
data, but also provide subjective rating scores from 40 human volunteers. The
dataset is mixed of driving simulator scenes and real-road ones.
High-interaction situations are designed and filtered to ensure behavior
diversity. Through data organization, analysis, and preprocessing, D2E contains
over 1100 segments of interactive driving case data covering from human driver
factor to evaluation results, supporting the development of data-driven
decision-making related algorithms.
