A Cooperative Fusion Architecture for Robust Localization: Application to Autonomous Driving

A Cooperative Fusion Architecture for Robust Localization: Application to Autonomous Driving
The localization of a vehicle is a central task of autonomous driving. Most of the time, it is solved by considering a single algorithm with a few sensors. In this paper, we propose a cooperative fusion architecture based on two main algorithms: a laser-based Simultaneous Localization And Mapping (SLAM) process and a lane detection and tracking approach using a single camera. Both algorithms are designed individually as cooperative fusion processes where other sensors (GPS and proprioceptive information) and dedicated maps are integrated to strengthen the advantages of each system. The whole architecture is formalized around key components (ego-vehicle, roadway, obstacle and environment). A final decision layer, that takes into account the state of each algorithm, allows the system to choose the most appropriate ego-vehicle localization mean based on the current road situation and the environmental context.

Guillaume Bresson, Mohamed-Cherif Rahal, Dominique Gruyer, Marc Revilloud, Zayed Alsayed. A Cooperative Fusion Architecture for Robust Localization: Application to Autonomous Driving. IEEE Intelligent Transportation Systems Conference 2016, Nov 2016, Rio de Janeiro, Brazil.<hal-01379322>