  Autonomous vehicles (AVs) must share the driving space with other drivers and
often employ conservative motion planning strategies to ensure safety. These
conservative strategies can negatively impact AV's performance and
significantly slow traffic throughput. Therefore, to avoid conservatism, we
design an interaction-aware motion planner for the ego vehicle (AV) that
interacts with surrounding vehicles to perform complex maneuvers in a locally
optimal manner. Our planner uses a neural network-based interactive trajectory
predictor and analytically integrates it with model predictive control (MPC).
We solve the MPC optimization using the alternating direction method of
multipliers (ADMM) and prove the algorithm's convergence. We provide an
empirical study and compare our method with a baseline heuristic method.
