Predicting pedestrian behavior is one of the main challenges for intelligent driving systems. In this paper, we present a new paradigm for evaluating egocentric pedestrian trajectory prediction algorithms. Based on various contextual information, we extract driving scenarios for a meaningful and systematic approach to identifying challenges for prediction models. In this regard, we also propose a new metric for more effective ranking within the scenario-based evaluation. We conduct extensive empirical studies of existing models on these scenarios to expose shortcomings and strengths of different approaches. The scenario-based analysis highlights the importance of using multimodal sources of information and challenges caused by inadequate modeling of ego-motion and scale of pedestrians. To this end, we propose a novel egocentric trajectory prediction model that benefits from multimodal sources of data fused in an effective and efficient step-wise hierarchical fashion and two auxiliary tasks designed to learn more robust representation of scene dynamics. We show that our approach achieves significant improvement by up to 40% in challenging scenarios compared to the past arts via empirical evaluation on common benchmark datasets.

翻译：预测行人行为是智能驾驶系统面临的主要挑战之一。本文提出了一种评估自我中心行人轨迹预测算法的新范式。基于多种上下文信息，我们提取驾驶场景，以有意义且系统化的方式识别预测模型面临的挑战。为此，我们还提出了一种新指标，用于在基于场景的评估中实现更有效的排名。我们在这些场景中对现有模型进行了广泛的实证研究，以揭示不同方法的优缺点。基于场景的分析强调了使用多模态信息源的重要性，以及由自我运动与行人尺度建模不足引发的挑战。针对这一问题，我们提出了一种新颖的自我中心轨迹预测模型，该模型通过高效且有效的逐步分层方式融合多模态数据源，并设计了两个辅助任务以学习更鲁棒的场景动态表征。实证结果表明，在通用基准数据集上，我们的方法在挑战性场景中相比现有技术实现了高达40%的显著性能提升。