The state-of-the-art decision and planning approaches for autonomous vehicles have moved away from manually designed systems, instead focusing on the utilisation of large-scale datasets of expert demonstration via Imitation Learning (IL). In this paper, we present a comprehensive review of IL approaches, primarily for the paradigm of end-to-end based systems in autonomous vehicles. We classify the literature into three distinct categories: 1) Behavioural Cloning (BC), 2) Direct Policy Learning (DPL) and 3) Inverse Reinforcement Learning (IRL). For each of these categories, the current state-of-the-art literature is comprehensively reviewed and summarised, with future directions of research identified to facilitate the development of imitation learning based systems for end-to-end autonomous vehicles. Due to the data-intensive nature of deep learning techniques, currently available datasets and simulators for end-to-end autonomous driving are also reviewed.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles


    Beteiligte:
    Le Mero, Luc (Autor:in) / Yi, Dewei (Autor:in) / Dianati, Mehrdad (Autor:in) / Mouzakitis, Alexandros (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.09.2022


    Format / Umfang :

    4538126 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Vision-Based Trajectory Planning via Imitation Learning for Autonomous Vehicles

    Cai, Peide / Sun, Yuxiang / Chen, Yuying et al. | IEEE | 2019


    Robust Behavioral Cloning for Autonomous Vehicles Using End-to-End Imitation Learning

    Samak, Chinmay Vilas / Kandhasamy, Sivanathan / Samak, Tanmay Vilas | SAE Technical Papers | 2021


    Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning

    Samak, Tanmay Vilas / Samak, Chinmay Vilas / Kandhasamy, Sivanathan | ArXiv | 2020

    Freier Zugriff

    Dynamic Conditional Imitation Learning for Autonomous Driving

    Eraqi, Hesham M. / Moustafa, Mohamed N. / Honer, Jens | IEEE | 2022