用多项式表示高效生成交通场景未来，兼顾准确与多样性。
EP-Diffuser: An Efficient Diffusion Model for Traffic Scene Generation and Prediction via Polynomial Representations
- 基于多项式表示的扩散模型，参数量更小但生成能力更强
- 在Argoverse 2上实现高精度且多样化的轨迹预测
- 适合自动驾驶场景中对多路径规划的需求
随着预测时长增加，由于代理行为的多模态特性，交通场景的未来演化预测变得愈发困难。现有主流预测模型主要关注最可能的未来轨迹，但为保障自动驾驶安全，覆盖合理运动可能性的分布同样重要。为此，我们提出EP-Diffuser，一种新型参数高效的基于扩散的生成模型，用于捕捉交通场景演化的可能分布。该模型以道路布局和代理历史为条件，作为预测器生成多样化且合理的场景延续。我们在Argoverse 2数据集上将EP-Diffuser与两种先进模型进行对比，评估其预测准确性和合理性。尽管模型规模显著更小，其仍实现了高度准确且可信的交通场景预测。进一步在Waymo Open数据集的分布外测试中评估泛化能力，结果表明本方法具有更优的鲁棒性。代码与模型检查点已开源：https://github.com/continental/EP-Diffuser。
原文摘要 · Abstract (English)
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 accuracy and plausibility of predictions on the Argoverse 2 dataset. Despite its significantly smaller model size, our approach achieves both highly accurate and plausible traffic scene predictions. We further evaluate model generalization ability in an out-of-distribution (OoD) test setting using Waymo Open dataset and show superior robustness of our approach. The code and model checkpoints are available at: https://github.com/continental/EP-Diffuser.
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