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A survey on imitation learning techniques for end-to-end autonomous vehicles
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Le Mero, Luc, Yi, Dewei, Dianati, Mehrdad and Mouzakitis, Alexandros (2022) A survey on imitation learning techniques for end-to-end autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 23 (9). pp. 14128-14147. doi:10.1109/TITS.2022.3144867 ISSN 1524-9050.
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Official URL: http://dx.doi.org/10.1109/TITS.2022.3144867
Abstract
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.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
Journal or Publication Title: | IEEE Transactions on Intelligent Transportation Systems | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 1524-9050 | ||||||||
Official Date: | September 2022 | ||||||||
Dates: |
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Volume: | 23 | ||||||||
Number: | 9 | ||||||||
Page Range: | pp. 14128-14147 | ||||||||
DOI: | 10.1109/TITS.2022.3144867 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access |
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