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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 ISSN 1524-9050.
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WRAP-Deep-learning-based-vehicle-behaviour-prediction-autonomous-driving-Jennings-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1764Kb) | Preview |
Official URL: https://doi.org/10.1109/TITS.2020.3012034
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 | |||||||||
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| Subjects: | Q Science > Q Science (General) T Technology > TE Highway engineering. Roads and pavements T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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| 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: |
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| 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 | |||||||||
| Date of first compliant deposit: | 13 August 2020 | |||||||||
| Date of first compliant Open Access: | 13 August 2020 | |||||||||
| RIOXX Funder/Project Grant: |
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