D2E-An Autonomous Decision-making Dataset involving Driver States and Human Evaluation

TL;DR

The D2E dataset provides comprehensive driver, vehicle, and environment data, including human evaluations, to enhance autonomous decision-making research with diverse, high-interaction driving scenarios.

Contribution

This paper introduces the D2E dataset, integrating driver states, environmental data, and human evaluations, addressing gaps in existing datasets for autonomous driving research.

Findings

Contains over 1100 interactive driving segments.

Includes driver physiological signals and eye attention data.

Provides subjective ratings from 40 human reviewers.

Abstract

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.