A COMPUTATIONAL MODEL OF DRIVER DECISION MAKING AT AN INTERSECTION CONTROLLED BY A TRAFFIC LIGHT

An important challenge associated with driving simulation development is the computational representation of agent behaviors. This paper describes the development of a preliminary autonomous agent behavior model (based on the Recognition-Primed Decision making model, and Hintzman's multiple-trace memory model) mimicking human decision making in approaching an intersection controlled by a traffic light. To populate the model, an initial Cognitive Task Analysis was conducted with six drivers to learn the important cues, expectancies, goals, and courses of action associated with traffic light approach. The agent model learns to associate environmental cues (such as traffic light color) with expectancies of upcoming events (like light color change) and appropriate courses of action (such as decelerating). The model is currently being evaluated for its successful representation of the Recognition-Primed Decision making process.