AUTODRAITEC: An Infrastructure-Based AUTOnomous DRiving System Using Artificial Intelligence and TEleCommunication Technologies

AUTODRAITEC: An Infrastructure-Based AUTOnomous DRiving System Using Artificial Intelligence and TEleCommunication Technologies

Zine el abidine Kherroubi, Fouzi Boukhalfa, Thierry Lestable, Carlos-Faouzi Bader

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8704-8707. https://doi.org/10.24963/ijcai.2024/1013

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.
Keywords:
Machine Learning: ML: Deep reinforcement learning
Machine Learning: ML: Explainable/Interpretable machine learning
Multidisciplinary Topics and Applications: MDA: Transportation
Robotics: ROB: Motion and path planning