BIROn - Birkbeck Institutional Research Online

    Road tracking using deep reinforcement learning for self-driving car applications

    Al-Nima, R. and Han, Tingting and Chen, Taolue (2019) Road tracking using deep reinforcement learning for self-driving car applications. In: The 11th International Conference on Computer Recognition Systems, 20-22 May 2019, Polanica-Zdroj, Poland. (In Press)

    [img]
    Preview
    Text
    ex18_llncs.pdf - Author's Accepted Manuscript

    Download (2MB) | Preview

    Abstract

    Deep reinforcement learning has received wide attentions recently. It combines deep learning with reinforcement learning and shows to be able to solve unprecedented challenging tasks. This paper proposes an efficient approach based on deep reinforcement learning to tackle the road tracking problem arisen from self-driving car applications. We propose a new neural network which collects input states from forward car facing views and produces suitable road tracking actions. The actions are derived from encoding the tracking directions and movements. We perform extensive experiments and demonstrate the efficacy of our approach. In particular, our approach has achieved 93.94% driving accuracy, outperforming the state-of-the-art approaches in literature.

    Metadata

    Item Type: Conference or Workshop Item (Paper)
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Tingting Han
    Date Deposited: 26 Jun 2019 12:44
    Last Modified: 27 Jul 2019 12:46
    URI: http://eprints.bbk.ac.uk/id/eprint/26630

    Statistics

    Downloads
    Activity Overview
    29Downloads
    43Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item