The increasing presence of autonomous vehicles (AVs) in urban environments introduces both opportunities and challenges, particularly regarding their interactions with traditional vehicles and other road users. This paper presents a comprehensive framework designed to assess the integration of AVs in mixed traffic scenarios. The framework is built upon real-world data collected from AV trials conducted in Turin, Italy. By leveraging traffic microsimulation along with machine learning techniques, the study proposes a framework aimed at assessing ex-ante the impacts on traffic of AVs introduction, thus constituting a relevant tool of virtual testing of CCAM (Cooperative, Connected, and Automated Mobility) trials before the physical introduction of autonomous vehicles on public roads. The integration of High-Performance Computing (HPC) ensures the efficiency of these simulations, enabling real-time analysis and testing. The proposed framework not only provides decision-makers with a tool for virtual testing of AV deployment, but also offers actionable insights into traffic management strategies. The study’s findings contribute to a deeper understanding of the role AVs can play in future urban mobility systems, particularly as cities prepare for the broader adoption of CCAM technologies.
A Data-Driven Integrated Framework for Virtual Testing of Autonomous Vehicles in Mixed Traffic Scenarios
Lect.Notes Social.Inform.
International Conference on Intelligent Transport Systems ; 2024 ; Pisa, Italy December 05, 2024 - December 06, 2024
2025-04-03
18 pages
Article/Chapter (Book)
Electronic Resource
English
British Library Conference Proceedings | 2022
|British Library Conference Proceedings | 2022
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