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A Survey of Deep Learning Applications to Autonomous Vehicle Control

Kuutti, Sampo, Bowden, Richard, Jin, Yaochu, Barber, Phil and Fallah, Saber (2020) A Survey of Deep Learning Applications to Autonomous Vehicle Control IEEE Transactions on Intelligent Transportation Systems.

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Abstract

Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Authors :
NameEmailORCID
Kuutti, Sampos.j.kuutti@surrey.ac.uk
Bowden, RichardR.Bowden@surrey.ac.uk
Jin, YaochuYaochu.Jin@surrey.ac.uk
Barber, Phil
Fallah, Sabers.fallah@surrey.ac.uk
Date : 7 January 2020
Funders : EPSRC - Engineering and Physical Sciences Research Council, Jaguar Land Rover
DOI : 10.1109/TITS.2019.2962338
Copyright Disclaimer : © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Machine learning; Neural networks; Intelligent control; Computer vision; Advanced driver assistance; Autonomous vehicles
Depositing User : Diane Maxfield
Date Deposited : 28 Jan 2020 15:06
Last Modified : 28 Jan 2020 15:06
URI: http://epubs.surrey.ac.uk/id/eprint/853456

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