A Framework for Risk-Aware Routing of Connected Vehicles via Artificial Intelligence

Abstract

The advent of Connected Autonomous Vehicles can enable the use of Artificial Intelligence (AI) techniques to support urban traffic controllers in extending their control capabilities with the ability to distribute vehicles in a urban region. Vehicles can communicate their destination, and receive an optimised route by traffic controllers. While the benefits of traffic routing are clear, it is also clear that re-routing has the potential to increase risks for vehicles' and passengers' safety due to environmental or urban factors. There is however a lack of work in the area of risk-aware routing.To fill the above-mentioned gap, we introduce a framework to incorporate risk-awareness in the vehicle routing process. The proposed framework provides a principled structure to define and characterise different classes of risk that can arise in a region, allowing to take them into account when generating routes. We show how this framework can be implemented, and we provide an empirical analysis of its performance on two European urban areas.