  Predicting the behaviors of other road users is crucial to safe and
intelligent decision-making for autonomous vehicles (AVs). However, most motion
prediction models ignore the influence of the AV's actions and the planning
module has to treat other agents as unalterable moving obstacles. To address
this problem, this paper proposes an interaction-aware motion prediction model
that is able to predict other agents' future trajectories according to the ego
agent's future plan, i.e., their reactions to the ego's actions. Specifically,
we employ Transformers to effectively encode the driving scene and incorporate
the AV's plan in decoding the predicted trajectories. To train the model to
accurately predict the reactions of other agents, we develop an online learning
framework, where the ego agent explores the environment and collects other
agents' reactions to itself. We validate the decision-making and learning
framework in three highly interactive simulated driving scenarios. The results
reveal that our decision-making method significantly outperforms the
reinforcement learning methods in terms of data efficiency and performance. We
also find that using the interaction-aware model can bring better performance
than the non-interaction-aware model and the exploration process helps improve
the success rate in testing.
