自动驾驶视觉数据集中的属性标注与偏差评估
Attribute Annotation and Bias Evaluation in Visual Datasets for Autonomous Driving
AI总结：
针对自动驾驶视觉感知数据集的公平性问题，本文提出受保护属性标注方法与工具，覆盖9万余人和5万余辆车，并揭示多类群体代表性不足。
摘要：
本文探讨自动驾驶领域中常被忽视的公平性问题，尤其是在基于视觉的感知与预测系统中，这些系统对自动驾驶汽车（AVs）的整体运行起着关键作用。我们将分析重点放在用于训练人员与车辆检测系统的一些最常用视觉数据集中存在的偏差上。我们提出一种标注方法和一个专用标注工具，二者均旨在标注视觉数据集中智能体的受保护属性。我们通过标注者间一致性分析验证了该方法，并给出了所有数据集中的属性分布。这些标注包括对超过9万人的年龄、性别、肤色、群体和交通方式属性的标注，以及对超过5万辆车的车辆类型、颜色和汽车类型属性的标注。总体而言，大多数属性的多样性非常低，某些群体，如儿童、轮椅使用者或个人移动车辆使用者，在所分析的数据集中代表性严重不足。本研究为在AVs感知与预测系统评估中考虑公平性的相关努力作出了重要贡献。本文遵循可复现性原则。标注工具、脚本和已标注属性可在 https://github.com/ec-jrc/humaint_annotator 公开获取。
英文摘要：
This paper addresses the often overlooked issue of fairness in the autonomous driving domain, particularly in vision-based perception and prediction systems, which play a pivotal role in the overall functioning of Autonomous Vehicles (AVs). We focus our analysis on biases present in some of the most commonly used visual datasets for training person and vehicle detection systems. We introduce an annotation methodology and a specialised annotation tool, both designed to annotate protected attributes of agents in visual datasets. We validate our methodology through an inter-rater agreement analysis and provide the distribution of attributes across all datasets. These include annotations for the attributes age, sex, skin tone, group, and means of transport for more than 90K people, as well as vehicle type, colour, and car type for over 50K vehicles. Generally, diversity is very low for most attributes, with some groups, such as children, wheelchair users, or personal mobility vehicle users, being extremely underrepresented in the analysed datasets. The study contributes significantly to efforts to consider fairness in the evaluation of perception and prediction systems for AVs. This paper follows reproducibility principles. The annotation tool, scripts and the annotated attributes can be accessed publicly at https://github.com/ec-jrc/humaint_annotator.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
