Variable-Drift Diffusion Models of Pedestrian Road-Crossing Decisions

Jami Pekkanen, Oscar Terence Giles, Yee Mun Lee, Ruth Madigan, Tatsuru Daimon, Natasha Merat, Gustav Markkula

研究成果: Article査読

13 被引用数 (Scopus)


Human behavior and interaction in road traffic is highly complex, with many open scientific questions of high applied importance, not least in relation to recent development efforts toward automated vehicles. In parallel, recent decades have seen major advances in cognitive neuroscience models of human decision-making, but these models have mainly been applied to simplified laboratory tasks. Here, we demonstrate how variable-drift extensions of drift diffusion (or evidence accumulation) models of decision-making can be adapted to the mundane yet non-trivial scenario of a pedestrian deciding if and when to cross a road with oncoming vehicle traffic. Our variable-drift diffusion models provide a mechanistic account of pedestrian road-crossing decisions, and how these are impacted by a variety of sensory cues: time and distance gaps in oncoming vehicle traffic, vehicle deceleration implicitly signaling intent to yield, as well as explicit communication of such yielding intentions. We conclude that variable-drift diffusion models not only hold great promise as mechanistic models of complex real-world decisions, but that they can also serve as applied tools for improving road traffic safety and efficiency.

ジャーナルComputational Brain and Behavior
出版ステータスPublished - 2022 3月

ASJC Scopus subject areas

  • 神経心理学および生理心理学
  • 発達心理学および教育心理学


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