Towards Metric-Agnostic Trajectory Forecasting

• URL: http://arxiv.org/abs/2607.01133v1
• Date: Wed, 01 Jul 2026 16:20:43 GMT
• Title: Towards Metric-Agnostic Trajectory Forecasting
• Abstract summary: Current trajectory forecasting models tailor their training objectives to the different benchmark metrics.<n>Because these metrics encourage conflicting behavior, we propose a paradigm change for trajectory forecasting.<n>We introduce Trajectory Distribution Evaluation (TraDiE) policies, metric-specific policies that map a predictive distribution to the set of $K$ and confidences required by trajectory forecasting metrics.
• Score: 19.092949647842165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate trajectory forecasting of surrounding traffic participants is a core capability for autonomous driving, enabling vehicles to anticipate behavior and plan safe maneuvers. We observe that current state-of-the-art forecasting models on Argoverse 2 and the Waymo Open Motion Dataset tailor their training objectives to the different benchmark metrics. Because these metrics encourage conflicting behavior, we propose a paradigm change for trajectory forecasting: training models with metric-agnostic probabilistic objectives and treating metric optimization 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 the set of $K$ trajectories and confidences required by trajectory forecasting metrics. We evaluate this framework by introducing DONUT-NLL, which adapts the training objective of the state-of-the-art trajectory forecasting model DONUT to directly optimize the predictive distribution. Using our policies, DONUT-NLL achieves state-of-the-art results on all metrics of the Waymo motion prediction benchmark.

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