Unveiling the Blind Spots: A Critical Examination of Fairness in Autonomous Driving Systems

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.