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

Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications