Thesis of Zine El Abidine Kherroubi


Subject:
Novel off-board decision-making strategy for connected and autonomous vehicles (Use case: Highway on-ramp merging)

Summary:

Merging in the highway on-ramp is a significant challenge toward realizing fully automated driving (level 4 of autonomous driving). The combination of communication technology and autonomous driving technology, which underpins the notion of Connected Autonomous Vehicles (CAVs), may improve greatly safety performances when performing highway on-ramp merging. However, even with the emergence of CAVs vehicles, some keys constraints should be considered to achieve a safe on-ramp merging. First, human-driven vehicles will still be present on the road, and it may take decades before all the commercialized vehicles will be fully autonomous and connected. Also, on-board vehicle sensors may provide inaccurate or incomplete data due to sensors limitations and blind spots, especially in such critical situations. To resolve these issues, the present thesis introduces a novel solution that uses an off-board Road-Side Unit (RSU) to realize fully automated highway on-ramp merging for connected and automated vehicles. Our proposed approach is based on an Artificial Neural Network (ANN) to predict drivers’ intentions. This prediction is used as an input state to a Deep Reinforcement Learning (DRL) agent that outputs the longitudinal acceleration for the merging vehicle. To achieve this, we first show how the road-side unit may be used to enhance perception in the on-ramp zone. We then propose a driver intention model that can predict the behavior of the human-driven vehicles in the main highway lane, with 99% accuracy. We use the output of this model as an input state to train a Twin Delayed Deep Deterministic Policy Gradients (TD3) agent that learns « safe » and « cooperative » driving policy to perform highway on-ramp merging. We show that our proposed decision-making strategy improves performance compared to the solutions proposed previously.


Advisor: Samir Aknine

Defense date: wednesday, december 16, 2020

Jury:
Mr Doshi Prashant Professeur(e)Université de Georgia, USARapporteur(e)
Mme Merghem-Boulahia LeilaProfesseur(e)Université de technologie de TroyesRapporteur(e)
Mr Kheddouci HamamacheProfesseur(e)Université Claude Bernard Lyon 1Examinateur​(trice)
Mr Mandiau ReneProfesseur(e)Université de Polytechnique Hauts-de-FranceExaminateur​(trice)
Mr Aknine Samir Professeur(e)Université Claude Bernard Lyon 1Directeur(trice) de thèse
Mme Bacha Rebiha ChercheurConnected Services Product Owner, Groupe RenaultCo-directeur (trice)