Parking is one basic function of autonomous vehicles. However, parking still remains difficult to be implemented, since it requires to generate a relatively long-term series of actions to reach a certain objective under complicated constraints. One recently proposed method used deep neural networks(DNN) to learn the relationship between the actual parking trajectories and the corresponding steering actions, so as to find the best parking trajectory via direct recalling. However, this method can only handle a special vehicle whose dynamic parameters are well known. In this paper, we use transfer learning technique to further extend this direct trajectory planning method and master general parking skills. We aim to mimic how human drivers make parking by using a specially designed deep neural network. The first few layers of this DNN contain the general parking trajectory planning knowledge for all kinds of vehicles; while the last few layers of this DNN can be quickly tuned to adapt various kinds of vehicles. Numerical tests show that, combining transfer learning and direct trajectory planning solution, our new approach enables automated vehicles to convey the knowledge of trajectory planning from one vehicle to another with a few try-and-tests.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Master general parking skill via deep learning


    Beteiligte:
    Lin, Yi-Lun (Autor:in) / Li, Li (Autor:in) / Dai, Xing-Yuan (Autor:in) / Zheng, Nan-Ning (Autor:in) / Wang, Fei-Yue (Autor:in)


    Erscheinungsdatum :

    01.06.2017


    Format / Umfang :

    846453 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Master General Parking Skill Via Deep Learning

    Lin, Yi-Lun / Li, Li / Dai, Xing-Yuan et al. | British Library Conference Proceedings | 2017


    Parking Analytics Framework using Deep Learning

    Benjdira, Bilel / Koubaa, Anis / Boulila, Wadii et al. | ArXiv | 2022

    Freier Zugriff

    Parking Analytics Framework using Deep Learning

    Benjdira, Bilel / Koubaa, Anis / Boulila, Wadii et al. | IEEE | 2022


    Understanding of Skill Difference on Parking Behavior Using Chunk Analysis

    Nemoto, H. / Kusayanagi, Y. / Jidaosha Gijutsukai | British Library Conference Proceedings | 2009


    Deep Learning-Based Mobile Parking Control System

    HUH BYOUNGHWEH / CHUNG SANGWOON / CHO TAIHOON et al. | Europäisches Patentamt | 2021

    Freier Zugriff