  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.
