  In this paper, we present a data-driven approach for safely predicting the
future state sets of pedestrians. Previous approaches to predicting the future
state sets of pedestrians either do not provide safety guarantees or are overly
conservative. Moreover, an additional challenge is the selection or
identification of a model that sufficiently captures the motion of pedestrians.
To address these issues, this paper introduces the idea of splitting previously
collected, historical pedestrian trajectories into different behavior modes for
performing data-driven reachability analysis. Through this proposed approach,
we are able to use data-driven reachability analysis to capture the future
state sets of pedestrians, while being less conservative and still maintaining
safety guarantees. Furthermore, this approach is modular and can support
different approaches for behavior splitting. To illustrate the efficacy of the
approach, we implement our method with a basic behavior-splitting module and
evaluate the implementation on an open-source data set of real pedestrian
trajectories. In this evaluation, we find that the modal reachable sets are
less conservative and more descriptive of the future state sets of the
pedestrian.
