  Trajectory prediction is an important task in autonomous driving.
State-of-the-art trajectory prediction models often use attention mechanisms to
model the interaction between agents. In this paper, we show that the attention
information from such models can also be used to measure the importance of each
agent with respect to the ego vehicle's future planned trajectory. Our
experiment results on the nuPlans dataset show that our method can effectively
find and rank surrounding agents by their impact on the ego's plan.
