Conformalized Adaptive Forecasting of Heterogeneous Trajectories

This paper presents a new conformal method for generatingsimultaneousforecasting bands guaranteed to cover theentire pathof a new random trajectory with sufficiently high probability. Prompted by the need for dependable uncertainty estimates in motion planning applications where the behavior of diverse objects may be more or less unpredictable, we blend different techniques from online conformal prediction of single and multiple time series, as well as ideas for addressing heteroscedasticity in regression. This solution is both principled, providing precise finite-sample guarantees, and effective, often leading to more informative predictions than prior methods.