ParetoTail: Distributional Pareto Adaptation for Long-Tail Motion Forecasting

Motion forecasting in naturalistic driving is inherently long-tailed, leaving rare yet safety-critical behaviors severely underrepresented. Existing approaches address this problem either by strengthening tail-sample learning or by generating additional rare behaviors, both increasing the influence of underrepresented behaviors during training. However, we observe a long-tail Pareto problem: stronger tail learning can improve rare-behavior forecasting while destabilizing predictions over the naturalistic distribution. We present ParetoTail, a framework that combines controllable tail generation with distributional Pareto adaptation. ParetoTail represents driving behavior in a continuous space of longitudinal motion, lateral motion, and interaction, and uses a conditional diffusion model to generate diverse rare trajectories in sparsely covered regions. To incorporate these behaviors without unrestricted distributional drift, we model a deficit-weighted tail correction field together with a naturalistic prediction geometry, whose interaction defines a compact tail-efficient adaptation space. Within this space, ParetoTail seeks a distributionally Pareto-dominating update when tail and naturalistic objectives admit co-improvement; otherwise, it maximizes tail improvement under explicit naturalistic-performance and prediction-drift constraints. Experiments demonstrate that ParetoTail improves forecasting on challenging long-tail behaviors while preserving strong performance on common naturalistic scenarios, with generated trajectories remaining controllable, diverse, and physically plausible.