Abstract

Computational models have emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior. In this paper we investigate the advantages of developing rigorous computational models of driver behavior in cognitive architectures — computational frameworks with underlying psychological theories that incorporate basic properties and limitations of the human system. In particular, we describe an integrated driver model developed in the ACT-R cognitive architecture and demonstrate how this model accounts for the steering profiles, lateral-position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing. The model has implications both for theoretical accounts of complex dynamic tasks in the context of cognitive architectures and for practical applications in predicting and recognizing driver behavior and distraction.

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