Autonomous driving systems have extended the spectrum of Web of Things for intelligent vehicles and have become an important component of the Web ecosystem. Similar to traditional Web-based applications, fairness is an essential aspect for ensuring the high quality of autonomous driving systems, particularly in the context of pedestrian detectors within them. However, there is an absence in the literature of a comprehensive assessment of the fairness of current Deep Learning (DL)-based pedestrian detectors. To fill the gap, we evaluate eight widely-explored DL-based pedestrian detectors across demographic groups on large-scale real-world datasets. To enable a thorough fairness evaluation, we provide extensive annotations for the datasets, resulting in 8,311 images with 16,070 gender labels, 20,115 age labels, and 3,513 skin tone labels. Our findings reveal significant fairness issues related to age. The undetected proportions for adults are 20.14% lower compared to children. Furthermore, we explore how various driving scenarios affect the fairness of pedestrian detectors. We find that the bias may exacerbate for children and females towards low brightness and low contrast.

翻译：自动驾驶系统扩展了智能车辆在物联网中的范畴，并已成为Web生态系统的重要组成部分。与传统Web应用类似，公平性是确保自动驾驶系统高质量的关键方面，尤其是其中的行人检测器。然而，现有文献缺乏对当前基于深度学习的行人检测器公平性的全面评估。为填补这一空白，我们在大规模真实数据集上，针对人口统计学群体评估了八种广泛研究的基于深度学习的行人检测器。为实现彻底的公平性评估，我们为数据集提供了大量标注，最终得到8,311张图像，包含16,070个性别标签、20,115个年龄标签和3,513个肤色标签。研究结果揭示了与年龄相关的显著公平性问题：成人的未检测比例较儿童低20.14%。此外，我们探讨了不同驾驶场景如何影响行人检测器的公平性，发现儿童和女性在低亮度和低对比度条件下，偏见可能进一步加剧。