Background Traffic Agents for Driving Simulators

Abstract

In the field of agent models for driving simulators, there are few models aimed at simulators that teach students how to drive. By approaching the problem from a students' perspective, we hope to increase the learning capacity of the driving simulator. Furthermore, none of the existing agent models use the Belief, Desire and Intention software model, which forms the basis of our work. By using BDI, we can exert more control over the agents while remaining to display realistic behaviour. We validate our method by presenting the behaviour of our agents to both driving school students and driving instructors. Results show that our model can produce signi?cantly deviating and realistic behaviour. Although surprisingly, it is deemed more cautious than intended. This thesis project was conducted as part of a collaboration between Utrecht University and GreenDino B.V.