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The development of automated vehicles has the potential to revolutionizetransportation, but they are currently unable to ensure a safe andtime-efficient driving style. Reliable models predicting human behavior areessential for overcoming this issue. While data-driven models are commonly usedto this end, they can be vulnerable in safety-critical edge cases. This has ledto an interest in models incorporating cognitive theory, but as such models arecommonly developed for explanatory purposes, this approach's effectiveness inbehavior prediction has remained largely untested so far. In this article, weinvestigate the usefulness of the \emph{Commotions} model -- a novelcognitively plausible model incorporating the latest theories of humanperception, decision-making, and motor control -- for predicting human behaviorin gap acceptance scenarios, which entail many important traffic interactionssuch as lane changes and intersections. We show that this model can competewith or even outperform well-established data-driven prediction models acrossseveral naturalistic datasets. These results demonstrate the promise ofincorporating cognitive theory in behavior prediction models for automatedvehicles.

Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study