Abstract: A system for trajectory prediction with agent prioritization is provided. A motion encoder is configured to determine future directions of agents of a plurality of agents and strengths of the agents based past trajectory information. The past trajectory information includes past trajectories of the agents at a number of time steps over a given time horizon for the agents. An inter-agent encoder is configured to determine the relations between the agents based on the future directions and the strengths. The relations identify motion impacts between the agents. The inter-agent encoder is further configured to calculate a motion prioritization score for each agent of the plurality of agents based on the relations between the agents. The motion prioritization scores define a priority order. The motion decoder is configured to calculate sequential predictions for future trajectories of the agents based on the motion prioritization scores.

Abstract: Systems and methods for uncertainty estimation in vehicle trajectory prediction are provided. In one embodiment, a method includes calculating predicted error scores for each static data point of a set of static data points for a new traffic scenario. A static data point corresponds to a location of the new traffic scenario. The method includes ranking the static data points of the set of static data points based on the predicted error scores. The method further includes selecting a predetermined percentage of the ranked static data points having the highest predicted error scores of the ranked static data points. The method includes identifying locations of the new traffic scenario corresponding to the selected ranked static data points. The method includes collecting additional data points from the identified locations of the new traffic scenario. The method includes training a decoder for a neural network based on the additional data points.