Towards Metric-Agnostic Trajectory Forecasting

Accurate trajectory forecasting of surrounding traffic par-ticipants is a core capability for autonomous driving, enabling vehiclesto anticipate behavior and plan safe maneuvers. We observe that cur-rent state-of-the-art forecasting models on Argoverse 2 and the WaymoOpen Motion Dataset tailor their training objectives to the differentbenchmark metrics. Because these metrics encourage conflicting behav-ior, we propose a paradigm change for trajectory forecasting: trainingmodels with metric-agnostic probabilistic objectives and treating metricoptimization as a downstream task applied to the predictive distribution.Concretely, we introduce Trajectory Distribution Evaluation (TraDiE)policies, metric-specific policies that map a predictive distribution to theset of K trajectories and confidences required by trajectory forecast-ing metrics. We evaluate this framework by introducing DONUT-NLL,which adapts the training objective of the state-of-the-art trajectory fore-casting model DONUT to directly optimize the predictive distribution.Using our policies, DONUT-NLL achieves state-of-the-art results on allmetrics of the Waymo motion prediction benchmark.