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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A cooperative fusion architecture for robust localization: Application to autonomous driving




    Publication date :

    2016-11-01


    Size :

    1310442 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Robust Data Fusion for Cooperative Vehicular Localization in Tunnels

    Hoang, Gia-Minh / Benoit, Denis / Haerri, Jerome et al. | British Library Conference Proceedings | 2017


    Robust data fusion for cooperative vehicular localization in tunnels

    Hoang, Gia-Minh / Denis, Benoit / Harri, Jerome et al. | IEEE | 2017


    Robust Localization for Autonomous Vehicles via Multisensor Fusion

    Alghooneh, Ahmad Reza / Panahandeh, Pouya / Ning, Minghao et al. | IEEE | 2024



    Layer-Based Multi-Sensor Fusion Architecture for Cooperative and Automated Driving Application Development

    Kwakkernaat, Maurice / Bijlsma, Tjerk / Ophelders, Frank | Springer Verlag | 2014