  In this paper, we delve into the pedestrian behavior understanding problem
from the perspective of three different tasks: intention estimation, action
prediction, and event risk assessment. We first define the tasks and discuss
how these tasks are represented and annotated in two widely used pedestrian
datasets, JAAD and PIE. We then propose a new benchmark based on these
definitions, available annotations, and three new classes of metrics, each
designed to assess different aspects of the model performance.
  We apply the new evaluation approach to examine four SOTA prediction models
on each task and compare their performance w.r.t. metrics and input modalities.
In particular, we analyze the differences between intention estimation and
action prediction tasks by considering various scenarios and contextual
factors. Lastly, we examine model agreement across these two tasks to show
their complementary role. The proposed benchmark reveals new facts about the
role of different data modalities, the tasks, and relevant data properties. We
conclude by elaborating on our findings and proposing future research
directions.
