Learning to Predict Mixed-Traffic Trajectories in Urban Scenarios from Little Training Data with Refined Environment Modeling

Trajectory prediction for autonomous driving has been extensively studied using large-scale datasets from the US and Asia. These datasets typically have a strong bias toward predicting vehicle motion. Recently, the View-of-Delft Prediction (VoD-P) dataset introduced a collection of European urban mixed-traffic scenarios, posing unique challenges due to its diversity and relatively small dataset size. In this work, we conduct a detailed study on trajectory prediction on the VoD-P dataset. We show that state-of-the-art trajectory prediction models, which perform well on large-scale vehicle-biased datasets, struggle to generalize to the scenarios. To address this limitation, we propose a simple yet effective transformer-based trajectory prediction model, specifically designed to handle the challenges posed in diverse urban scenarios. Combining a strong baseline with refined environment modeling, our approach significantly outperforms all existing methods on the VoD-P dataset.