  For automotive applications, the Graph Attention Network (GAT) is a
prominently used architecture to include relational information of a traffic
scenario during feature embedding. As shown in this work, however, one of the
most popular GAT realizations, namely GATv2, has potential pitfalls that hinder
an optimal parameter learning. Especially for small and sparse graph structures
a proper optimization is problematic. To surpass limitations, this work
proposes architectural modifications of GATv2. In controlled experiments, it is
shown that the proposed model adaptions improve prediction performance in a
node-level regression task and make it more robust to parameter initialization.
This work aims for a better understanding of the attention mechanism and
analyzes its interpretability of identifying causal importance.
