Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the discrete nature of human intent before predicting continuous trajectories, to improve accuracy and support explainability. However, these approaches often assume the intent to remain fixed over the prediction horizon, which is problematic in practice, especially over longer horizons. To overcome this limitation, we introduce HYPER, a general and expressive hybrid prediction framework that models evolving human intent. By modeling traffic agents as a hybrid discrete-continuous system, our approach is capable of predicting discrete intent changes over time. We learn the probabilistic hybrid model via a maximum likelihood estimation problem and leverage neural proposal distributions to sample adaptively from the exponentially growing discrete space. The overall approach affords a better trade-off between accuracy and coverage. We train and validate our model on the Argoverse dataset, and demonstrate its effectiveness through comprehensive ablation studies and comparisons with state-of-the-art models.
翻译:模拟多模式高层次意图对于确保轨迹预测的多样性十分重要。现有办法在预测连续轨迹之前,先探索人类意图的离散性质,然后预测连续轨迹,提高准确性,支持解释性。不过,这些办法往往假定意图在预测地平线上固定不变,这在实践上是有问题的,特别是在较长的地平线上。为了克服这一限制,我们引入了HYPER,这是一个通用和直观的混合预测框架,其模型将演变为人类意图。通过将交通物剂建模为混合离散连续系统,我们的方法能够预测离散意图随时间的变化。我们通过一个最大可能性估计问题了解概率混合模型,并利用神经建议分布,从急剧增长的离散空间进行适应性抽样。总体办法在准确性和覆盖之间提供了更好的权衡。我们在Argovers数据集上培训和验证我们的模型,并通过全面对比研究和与最新模型的比较来显示其有效性。