  Transformers have recently been utilized to perform object detection and
tracking in the context of autonomous driving. One unique characteristic of
these models is that attention weights are computed in each forward pass,
giving insights into the model's interior, in particular, which part of the
input data it deemed interesting for the given task. Such an attention matrix
with the input grid is available for each detected (or tracked) object in every
transformer decoder layer. In this work, we investigate the distribution of
these attention weights: How do they change through the decoder layers and
through the lifetime of a track? Can they be used to infer additional
information about an object, such as a detection uncertainty? Especially in
unstructured environments, or environments that were not common during
training, a reliable measure of detection uncertainty is crucial to decide
whether the system can still be trusted or not.
