EP-Diffuser: An Efficient Diffusion Model for Traffic Scene Generation and Prediction via Polynomial Representations

As the prediction horizon increases, predicting the future evolution of traffic scenes becomes increasingly difficult due to the multi-modal nature of agent motion. Most state-of-the-art (SotA) prediction models primarily focus on forecasting the most likely future. However, for the safe operation of autonomous vehicles, it is equally important to cover the distribution for plausible motion alternatives. To address this, we introduce EP-Diffuser, a novel parameter-efficient diffusion-based generative model designed to capture the distribution of possible traffic scene evolutions. Conditioned on road layout and agent history, our model acts as a predictor and generates diverse, plausible scene continuations. We benchmark EP-Diffuser against two SotA models in terms of plausibility, diversity, and accuracy of predictions on the Argoverse 2 dataset. Despite its significantly smaller model size, our approach achieves both highly plausible and diverse traffic scene predictions with comparable accuracy. We further evaluate model generalization in an out-of-distribution (OoD) test setting using Waymo Open dataset and show superior robustness of our approach.