AUTODRAITEC: A Novel AI-based System on the Road Infrastructure for Autonomous Driving - Proof of Concept
Zine el abidine Kherroubi, Fouzi Boukhalfa, Thierry Lestable · 2024
Road infrastructure has become a key enabler for achieving full autonomous driving. However, research on this topic is still awaiting answers, especially at the experimentation side. For this reason, we present AUTODRAITEC1, a novel AI-based system that is deployed on the road infrastructure to control the driving of Connected and Autonomous Vehicles (CAVs). 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 new architecture aims to enhance the situational awareness for autonomous driving systems, and improve the explainability of AI actions through the understanding of others human-drivers behaviors. Beside simulation evaluation, a Proof of Concept (PoC) of the system is presented. Using a 1:18 scale testbed that faithfully replicates real-world driving scenarios, we demonstrate that AUTODRAITEC consistently succeeds in avoiding accidents, enhancing safety distance and efficiency, while preserving the traffic flow rate. The presented solution is also scalable to different driving use cases.