Explaining Traffic Situations – Architecture of a Virtual Driving Instructor

EasyChair Preprint 3214

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.

Keyphrases: Artificial Intelligence, Explanations, FO logic, ITS, Ontology, Virtual driving instructor, driving instructor, multi-agent, multi-agent system, situation awareness

@booklet{EasyChair:3214,
  author    = {Martin Sandberg and Johannes Rehm and Matěj Mňouček and Irina Reshodko and Odd Erik Gundersen},
  title     = {Explaining Traffic Situations – Architecture of a Virtual Driving Instructor},
  howpublished = {EasyChair Preprint 3214},
  year      = {EasyChair, 2020}}