Path Planning for Autonomous Platoon Formation

Ouafae El Ganaoui-Mourlan (1) , Stephane Camp (2) , Thomas Hannagan (3) , Vaibhav Arora (2) , Martin de Neuville (2) , Vaios Andreas Kousournas (2)

1 IFPEN - IFP Energies nouvelles
2 IFP School
3 STELLANTIS

In the context of automated highway systems (AHS), this work proposes an approach that enables a vehicle to autonomously join a platoon with optimized trajectory in the presence of dynamical traffic obstacles. A notable aspect is the use of Model Predictive Control (MPC) optimization of the planned path, in conjunction with a variant of the Rapidly-exploring Random Trees (RRT*) algorithm for the purpose of platoon formation. This combination efficiently explores the space of possible trajectories, returning trajectories that are smoothened out with respect to the dynamic constraints of the vehicle, while at the same time allowing for real-time implementation. The implementation we propose takes into consideration both localization and mapping through relevant sensors and V2V communication. The complete algorithm is tested over various nominal and worst-case scenarios, qualifying the merits of the proposed methodology.

Mots clés: Autonomous vehicle; Platoon formation; Path planning; Model predictive control; RRT; Optimization

Domaines: Intelligence artificielle [cs.AI]; Sciences de l'Homme et Société

Ouafae El Ganaoui-Mourlan, Stephane Camp, Thomas Hannagan, Vaibhav Arora, Martin de Neuville, et al.. Path Planning for Autonomous Platoon Formation. Sustainability, 2021, 13 (9), pp.4668. ⟨10.3390/su13094668⟩. ⟨hal-03274738⟩