  Non-cooperative interactions commonly occur in multi-agent scenarios such as
car racing, where an ego vehicle can choose to overtake the rival, or stay
behind it until a safe overtaking "corridor" opens. While an expert human can
do well at making such time-sensitive decisions, the development of safe and
efficient game-theoretic trajectory planners capable of rapidly reasoning
discrete options is yet to be fully addressed. The recently developed nonlinear
opinion dynamics (NOD) show promise in enabling fast opinion formation and
avoiding safety-critical deadlocks. However, it remains an open challenge to
determine the model parameters of NOD automatically and adaptively, accounting
for the ever-changing environment of interaction. In this work, we propose for
the first time a learning-based, game-theoretic approach to synthesize a Neural
NOD model from expert demonstrations, given as a dataset containing (possibly
incomplete) state and action trajectories of interacting agents. The learned
NOD can be used by existing dynamic game solvers to plan decisively while
accounting for the predicted change of other agents' intents, thus enabling
situational awareness in planning. We demonstrate Neural NOD's ability to make
fast and robust decisions in a simulated autonomous racing example, leading to
tangible improvements in safety and overtaking performance over
state-of-the-art data-driven game-theoretic planning methods.
