Tail-MDP: Multi-Dimensional Long-Tail Learning with Prompt-Guided Decoding for Trajectory Prediction

Trajectory prediction datasets typically exhibit pronounced long-tail distributions, causing trained models to underperform in rare yet safety-critical scenarios. Existing long-tail learning methods mainly characterize long-tailedness from a single dimension, making them insufficient for driving scenarios with multi-dimensional tail properties. Moreover, they primarily focus on representation refinement while overlooking the decoding stage, leaving encoded tail cues insufficiently exploited for trajectory generation. To this end, we propose Tail-MDP, a multi-dimensional long-tail learning framework with tail-aware decoding for trajectory prediction. We first reformulate tail sample identification as an anomaly detection problem with respect to head-dominant patterns, and use autoencoder reconstruction errors as a unified criterion to quantify long-tailedness across three key dimensions affecting future motion—motion dynamics, social interactions, and topological constraints. Here, the autoencoders are refined through training sample selection and sparse reconstruction to improve estimation reliability. Based on the resulting multi-dimensional tailness estimates, we introduce dimension-wise contrastive learning to learn representations that capture diverse tail characteristics. Furthermore, we develop a prompt-guided decoding mechanism that generates prompts according to the sample’s tailness degree and injects them into the decoder via prefix tuning, thereby guiding the model to focus on tail cues during trajectory generation. Experiments on Argoverse2 and nuScenes show that Tail-MDP consistently improves performance on diverse tail scenarios, outperforms SOTA long-tail methods, and generalizes across competitive baselines.