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

Data-Driven Reachability Analysis of Pedestrians Using Behavior Modes