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

TLDR

The state‑of‑the‑art decision and planning for autonomous vehicles has shifted from manually designed systems to large‑scale expert demonstration via imitation learning. This paper reviews imitation‑learning approaches for end‑to‑end autonomous vehicle systems. The authors classify IL methods into behavioural cloning, direct policy learning, and inverse reinforcement learning, survey current state‑of‑the‑art literature and datasets, and outline future research directions. The review highlights existing work and proposes future research to advance imitation learning for end‑to‑end autonomous driving.

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.