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Deep learning-based vehicle behaviour prediction for autonomous driving applications : a review

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Mozaffari, Sajjad, Al-Jarrah, Omar Y., Dianati, Mehrdad, Jennings, Paul. A. and Mouzakitis, Alexandros (2022) Deep learning-based vehicle behaviour prediction for autonomous driving applications : a review. IEEE Transactions on Intelligent Transportation Systems, 23 (1). pp. 33-47. doi:10.1109/TITS.2020.3012034

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Official URL: https://doi.org/10.1109/TITS.2020.3012034

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Abstract

Behaviour prediction function of an autonomous vehicle predicts the future states of the nearby vehicles based on the current and past observations of the surrounding environment. This helps enhance their awareness of the imminent hazards. However, conventional behavior prediction solutions are applicable in simple driving scenarios that require short prediction horizons. Most recently, deep learning-based approaches have become popular due to their promising performance in more complex environments compared to the conventional approaches. Motivated by this increased popularity, we provide a comprehensive review of the state-of-the-art of deep learning-based approaches for vehicle behavior prediction in this article. We firstly give an overview of the generic problem of vehicle behavior prediction and discuss its challenges, followed by classification and review of the most recent deep learning-based solutions based on three criteria: input representation, output type, and prediction method. The article also discusses the performance of several well-known solutions, identifies the research gaps in the literature and outlines potential new research directions.

Item Type: Journal Article
Alternative Title:
Subjects: Q Science > Q Science (General)
T Technology > TE Highway engineering. Roads and pavements
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions: Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH): Computational intelligence, Intelligent transportation systems, Automated vehicles, Automated vehicles -- Decision making, Machine learning
Journal or Publication Title: IEEE Transactions on Intelligent Transportation Systems
Publisher: IEEE
ISSN: 1524-9050
Official Date: January 2022
Dates:
DateEvent
January 2022Published
4 August 2020Available
6 July 2020Accepted
Volume: 23
Number: 1
Page Range: pp. 33-47
DOI: 10.1109/TITS.2020.3012034
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): © 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.
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDJaguar Land RoverUNSPECIFIED
EP/N01300X/1 [EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
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