Generating a multiplicity of policies for agent steering in crowd simulation

Abstract

Pedestrian steering algorithms range from completely procedural to entirely data-driven, but the former grossly generalize across possible human behaviors and suffer computationally, whereas the latter are limited by the burden of ever-increasing data samples. Our approach seeks the balanced middle ground by deriving a collection of machine-learned policies… (More)
DOI: 10.1002/cav.1572

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