Cognition-Driven Traffic Simulation for Unstructured Road Networks

Abstract

Dynamic changes of traffic features in unstructured road networks challenge the scene-cognitive abilities of drivers, which brings various heterogeneous traffic behaviors. Modeling traffic with these heterogeneous behaviors would have significant impact on realistic traffic simulation. Most existing traffic methods generate traffic behaviors by adjusting parameters and cannot describe those heterogeneous traffic flows in detail. In this paper, a cognition-driven trafficsimulation method inspired by the theory of cognitive psychology is introduced. We first present a visual-filtering model and a perceptual-information fusion model to describe drivers’ heterogeneous cognitive processes. Then, logistic regression is used to model drivers’ heuristic decision-making processes based on the above cognitive results. Lastly, we apply the high-level cognitive decision-making results to low-level traffic simulation. The experimental results show that our method can provide realistic simulations for the traffic with those heterogeneous behaviors in unstructured road networks and has nearly the same efficiency as that of existing methods.