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.

本文提出了一种基于场景进行评估的新模式，以评估自主行驶系统中行人轨迹预测算法的挑战，并通过实证评估在多模态来源数据的融合和有效的分层方式下取得了显著的改进。