An ontology-based approach to relax traffic regulation for autonomous vehicle assistance

TL;DR

This paper presents an ontology-based system that helps autonomous vehicles make safe, high-level decisions to relax traffic regulations in extreme situations, enhancing traffic flow and safety.

Contribution

It introduces a novel high-level ontological representation and inference framework for autonomous vehicles to decide when to relax traffic rules in practical scenarios.

Findings

Effective in practical case studies

Enables safe relaxation of traffic regulations

Improves traffic flow in extreme situations

Abstract

Traffic regulation must be respected by all vehicles, either human- or computer- driven. However, extreme traffic situations might exhibit practical cases in which a vehicle should safely and reasonably relax traffic regulation, e.g., in order not to be indefinitely blocked and to keep circulating. In this paper, we propose a high-level representation of an automated vehicle, other vehicles and their environment, which can assist drivers in taking such "illegal" but practical relaxation decisions. This high-level representation (an ontology) includes topological knowledge and inference rules, in order to compute the next high-level motion an automated vehicle should take, as assistance to a driver. Results on practical cases are presented.