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 besides achieving state-of-the-art performance, our method significantly performs better on long-tail scenarios. We further conduct ablation studies to highlight the contribution of different proposed components.

翻译：行人的未来运动轨迹准确预测对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，现有自然场景轨迹预测数据集普遍偏向简单样本，缺乏复杂场景。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。现有方法采用对比学习和条件超网络等方式处理长尾问题，但这些方法缺乏模块化，难以应用于多种机器学习架构。本研究提出一种模块化、模型无关的轨迹预测框架，通过专业化专家混合机制实现。在该方法中，每位专家针对特定数据分布区域进行专业化技能训练。我们利用路由网络生成相对置信度分数以选择最优专家进行预测。在行人轨迹预测基准数据集上的实验表明，该方法不仅获得最优性能，在长尾场景中表现显著提升。我们进一步通过消融实验验证了各提出组件的贡献。