Abstract: Systems and methods are provided trajectory prediction that leverages game-theory to improve coverage of multi-modal predictions. Examples of the systems and methods include obtaining training data including first trajectories for a first plurality of agent devices and first map information of a first environment for a past time horizon and applying the training data to a game-theoretic mode-finding algorithm to generate a mode-finding model for each agent device that predicts modes of the first trajectories. A trajectory prediction model can be trained on the predicted modes as a coverage loss term between predicted modes. Future trajectories can be predicted for a second plurality of agent devices based on applying observed data to the trajectory prediction model. A control signal can then be generated to effectuate an autonomous driving command on an agent device of the second plurality of agent devices based on the predicted future trajectories.