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

Matteo Cardellini, Carmine Dodaro, Marco Maratea, Mauro Vallati

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of the 26th IEEE International Conference on Intelligent Transportation Systems
Subtitle of host publicationITSC 2023
Number of pages6
ISBN (Electronic)9798350399462
ISBN (Print)9798350399479
Publication statusPublished - 13 Feb 2024
Event26th IEEE International Conference on Intelligent Transportation Systems - Bilbao, Spain
Duration: 24 Sep 202328 Sep 2023
Conference number: 26


Conference26th IEEE International Conference on Intelligent Transportation Systems
Abbreviated titleITSC 2023
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