  Representation learning in recent years has been addressed with
self-supervised learning methods. The input data is augmented into two
distorted views and an encoder learns the representations that are invariant to
distortions -- cross-view prediction. Augmentation is one of the key components
in cross-view self-supervised learning frameworks to learn visual
representations. This paper presents ExAgt, a novel method to include expert
knowledge for augmenting traffic scenarios, to improve the learnt
representations without any human annotation. The expert-guided augmentations
are generated in an automated fashion based on the infrastructure, the
interactions between the EGO and the traffic participants and an ideal sensor
model. The ExAgt method is applied in two state-of-the-art cross-view
prediction methods and the representations learnt are tested in downstream
tasks like classification and clustering. Results show that the ExAgt method
improves representation learning compared to using only standard augmentations
and it provides a better representation space stability. The code is available
at https://github.com/lab176344/ExAgt.
