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.

通过深度学习技术的进步，数据驱动方法在自动驾驶的决策中越来越多地使用，而数据集的质量极大地影响了模型的性能。然而，尽管当前的数据集在车辆和环境数据的收集方面取得了重大进展，但对于包括驾驶员状态和人类评估在内的人为因素数据的重视还不够。此外，现有的数据集大部分由车辆跟随等简单场景组成，交互程度较低。本文介绍了一种名为 “Driver to Evaluation”（D2E）的自动决策数据集，其中包含驾驶员状态、车辆状态、环境情况以及来自人类评审人员的评估分数的数据，涵盖了车辆决策的全面过程。除了常规的代理和周围环境信息外，我们不仅收集了包括第一视角视频、生理信号和眼部注意力数据在内的驾驶员因素数据，还提供了来自 40 名志愿者的主观评分。该数据集包含了驾驶模拟器场景和真实道路场景的混合数据。设计和筛选了高交互情况以确保行为多样性。通过数据组织、分析和预处理，D2E 包含了超过 1100 个交互驾驶案例数据段，涵盖了从人类驾驶因素到评估结果的内容，为支持数据驱动的决策相关算法的开发提供了支持。