Trabajo presentado en la IEEE International Conference on Robotics and Automation (ICRA), celebrado en Brisbane (Australia), del 21 al 25 de mayo de 2018 ; Perception technologies in Autonomous Driving are experiencing their golden age due to the advances in Deep Learning. Yet, most of these systems rely on the semantically rich information of RGB images. Deep Learning solutions applied to the data of other sensors typically mounted on autonomous cars (e.g. lidars or radars) are not explored much. In this paper we propose a novel solution to understand the dynamics of moving vehicles of the scene from only lidar information. The main challenge of this problem stems from the fact that we need to disambiguate the proprio-motion of the “observer” vehicle from that of the external “observed” vehicles. For this purpose, we devise a CNN architecture which at testing time is fed with pairs of consecutive lidar scans. However, in order to properly learn the parameters of this network, during training we introduce a series of so-called pretext tasks which also leverage on image data. These tasks include semantic information about vehicleness and a novel lidar-flow feature which combines standard image-based optical flow with lidar scans. We obtain very promising results and show that including distilled image information only during training, allows improving the inference results of the network at test time, even when image data is no longer used. ; This work has been supported by the Spanish Ministry of Economy and Competitiveness projects HuMoUR (TIN2017-90086-R) and COLROBTRANSP (DPI2016-78957-R) and the Spanish State Research Agency through the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656). The authors also thank Nvidia for hardware donation under the GPU grant program. ; Peer reviewed


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Deep lidar CNN to understand the dynamics of moving vehicles



    Erscheinungsdatum :

    2018-01-01



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    LiDAR eyes for autonomous vehicles

    Thakker, T. / Pulikkaseril, C. / Lam, S. et al. | SPIE | 2019


    Text Recognition in Moving Vehicles using Deep learning Neural Networks

    Ilayarajaa, K.T. / Vijayakumar, V. / Sugadev, M. et al. | IEEE | 2021


    LIDAR based Detection of Small Vehicles

    Bhatlawande, Shripad / Shilaskar, Swati / Dhanawade, Amol | IEEE | 2022


    Detecting moving vehicles

    BUCHANAN RODERICK / REVELL JAMES DUNCAN | Europäisches Patentamt | 2016

    Freier Zugriff

    Tracking Military Vehicles to Better Understand Invasive Species Spread

    International Society for Terrain-Vehicle Systems | British Library Conference Proceedings | 2008