Calibration metrics for measuring trajectory prediction

Abstract

The present technology pertains to measuring the quality of an uncertainty prediction provided by a trajectory prediction algorithm. An AI/ML platform can receive data including predicted trajectories of objects received from the trajectory prediction algorithm and observed paths for the objects received from the plurality of sensors. The predicted trajectories indicate predicted locations of the objects at a plurality of time intervals, and uncertainty predictions reflecting an uncertainty about the location of the objects at each of the predicted time intervals. The AI/ML platform can further determine respective standard deviations of the observed paths of the objects relative to the respective uncertainty predictions and plots the respective standard deviations against an ideal distribution of path distributions relative to the predicted locations of the objects at the plurality of time intervals.