Accurate prediction of pedestrians' future motions is critical for intelligent driving systems. Developing models for this task requires rich datasets containing diverse sets of samples. However, the existing naturalistic trajectory prediction datasets are generally imbalanced in favor of simpler samples and lack challenging scenarios. Such a long-tail effect causes prediction models to underperform on the tail portion of the data distribution containing safety-critical scenarios. Previous methods tackle the long-tail problem using methods such as contrastive learning and class-conditioned hypernetworks. These approaches, however, are not modular and cannot be applied to many machine learning architectures. In this work, we propose a modular model-agnostic framework for trajectory prediction that leverages a specialized mixture of experts. In our approach, each expert is trained with a specialized skill with respect to a particular part of the data. To produce predictions, we utilise a router network that selects the best expert by generating relative confidence scores. We conduct experimentation on common pedestrian trajectory prediction datasets and show that our method improves performance on long-tail scenarios. We further conduct ablation studies to highlight the contribution of different proposed components.

翻译：行人未来运动轨迹的精确预测对于智能驾驶系统至关重要。开发此类模型需要包含多样化样本的丰富数据集。然而，现有的自然轨迹预测数据集普遍存在简单样本占优、缺乏挑战性场景的不平衡问题。这种长尾效应导致预测模型在包含安全关键场景的数据分布尾部表现不佳。现有方法通过对比学习和类别条件超网络等技术处理长尾问题，但这些方法缺乏模块化特性，难以迁移至多种机器学习架构。本文提出一种模块化且与模型无关的轨迹预测框架，该框架利用专门的专家混合机制。在该方法中，每个专家针对数据特定部分训练专用技能。为生成预测结果，我们采用路由网络通过生成相对置信度分数来选择最优专家。我们在常见行人轨迹预测数据集上开展实验，结果表明所提方法能提升长尾场景下的预测性能。进一步通过消融研究，我们验证了各设计组件的贡献。