{"type":"ScholarlyArticle","title":"AUTODRAITEC: An Infrastructure-Based AUTOnomous DRiving System Using Artificial Intelligence and TEleCommunication Technologies","authors":[{"name":"Zine el abidine Kherroubi","slug":"zine-el-abidine-kherroubi","given":"Zine el abidine","family":"Kherroubi","affiliation":"Technology Innovation Institute , 9639 Masdar City , Abu Dhabi , United Arab Emirates"},{"name":"Fouzi Boukhalfa","slug":"fouzi-boukhalfa","given":"Fouzi","family":"Boukhalfa","affiliation":"Technology Innovation Institute , 9639 Masdar City , Abu Dhabi , United Arab Emirates"},{"name":"Thierry Lestable","slug":"thierry-lestable","given":"Thierry","family":"Lestable","affiliation":"Technology Innovation Institute , 9639 Masdar City , Abu Dhabi , United Arab Emirates"},{"name":"Carlos-Faouzi Bader","slug":"carlos-faouzi-bader","given":"Carlos-Faouzi","family":"Bader","affiliation":"Technology Innovation Institute , 9639 Masdar City , Abu Dhabi , United Arab Emirates"}],"abstract":"This paper introduces AUTODRAITEC, a novel AI-based system that is deployed on the road infrastructure to control the driving of Connected and Autonomous Vehicles (CAVs). For this purpose, we present a convincing proof of concept that demonstrates the effectiveness of our solution. The system deploys a hybrid machine learning approach comprised of a supervised learning classifier to characterize the behaviors of human drivers, with a deep reinforcement learning policy to provide speed recommendations for CAVs. This system is implemented using perception sensors and an industrial computer (IPC), which are intended to be deployed on the road infrastructure. Using a 1:18 scale testbed that faithfully replicates real-world driving scenarios, we demonstrate that AUTODRAITEC improves driving safety and efficiency while preserving the traffic flow rate.","doi":"10.24963/ijcai.2024/1013","url":"https://ijcai.org/proceedings/2024/1013","pdf":"https://ijcai.org/proceedings/2024/1013.pdf","markdown":"https://ijcai.org/proceedings/2024/1013.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/1013","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":1013,"pages":{"first":8704,"last":8707},"keywords":["Machine Learning: ML: Explainable/Interpretable machine learning","Machine Learning: ML: Deep reinforcement learning","Multidisciplinary Topics and Applications: MDA: Transportation","Robotics: ROB: Motion and path planning"],"videos":[],"references":[{"position":1,"raw":"[Carreras et al., 2018] Anna Carreras, Xavier Daura, Jacque- line Erhart, and Stefan Ruehrup. 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