File(s) under permanent embargo

Reason: Publisher requirement. Embargo date will be added following publication.

Imitation learning based decision-making for autonomous vehicle control at traffic roundabouts

The essential of developing an advanced driving assistance system is to learn human-like decisions to enhance driving safety. When controlling a vehicle, joining roundabouts smoothly and timely is a challenging task even for human drivers. In this paper, we propose a novel imitation learning based decision making framework to provide recommendations to join roundabouts. Our proposed approach takes observations from a monocular camera mounted on vehicle as input and use deep policy networks to provide decisions when is the best timing to enter a roundabout. The domain expert guided learning framework can not only improve the decision-making but also speed up the convergence of the deep policy networks. We evaluate the proposed framework by comparing with state-of-the-art supervised learning methods, including conventional supervised learning methods, such as SVM and kNN, and deep learning based methods. The experimental results demonstrate that the imitation learning-based decision making framework, which ourperforms supervised learning methods, can be applied in driving assistance system to facilitate better decision-making when approaching roundabouts.

History

School

  • Science

Department

  • Computer Science

Published in

Multimedia Tools and Applications

Publisher

Springer

Version

AM (Accepted Manuscript)

Publisher statement

This paper was accepted for publication in the journal Multimedia Tools and Applications and the definitive published version is available at [insert DOI link].

Acceptance date

14/01/2022

ISSN

1380-7501

eISSN

1573-7721

Language

en

Depositor

Dr Hui Fang. Deposit date: 14 January 2022