Explaining Traffic Situations – Architecture of a Virtual Driving Instructor

Abstract

Intelligent tutoring systems become more and more common in assisting human learners. Distinct advantages of intelligent tutoring systems are personalized teaching tailored to each student, on-demand availability not depending on working hour regulations and standardized evaluation not subjective to the experience and biases of human individuals. A virtual driving instructor that supports driver training in a virtual world could conduct on-demand personalized teaching and standardized evaluation. We propose an architectural design of a virtual driving instructor system that can comprehend and explain complex traffic situations. The architecture is based on a multi-agent system capable of reasoning about traffic situations and explaining them at an arbitrary level of detail in real-time. The agents process real-time data to produce instances of concepts and relations in an ever-evolving knowledge graph. The concepts and relations are defined in a traffic situation ontology. Finally, we demonstrate the process of reasoning and generating explanations on an overtake scenario.