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A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles.
- Source :
- IEEE Transactions on Intelligent Transportation Systems; Sep2022, Vol. 23 Issue 9, p14128-14147, 20p
- Publication Year :
- 2022
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15249050
- Volume :
- 23
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Intelligent Transportation Systems
- Publication Type :
- Academic Journal
- Accession number :
- 159209297
- Full Text :
- https://doi.org/10.1109/TITS.2022.3144867