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Title: | Pedestrian and vehicle behaviour prediction in autonomous vehicle system — A review |
Authors: | Galvão, LG Huda, MN |
Keywords: | deep learning;autonomous vehicle;pedestrian;vehicles;behaviour prediction |
Issue Date: | 12-Oct-2023 |
Publisher: | Elsevier |
Citation: | Galvão, L.G. and Huda, M.N. (2024) 'Pedestrian and vehicle behaviour prediction in autonomous vehicle system — A review', Expert Systems with Applications, 238 (15 March 2024), 121983, pp. 1 - 29. doi: 10.1016/j.eswa.2023.121983. |
Abstract: | Copyright © 2023 The Author(s). Autonomous vehicles (AV)s have become a trending topic nowadays since they have the potential to solve traffic problems, such as accidents and congestion. Although AV systems have greatly evolved, it still have their limitations. For example, Google reported that their AVs have been involved in several collisions and near misses. While most of these collisions and near misses were caused by third parties, the AVs should be able to predict and avoid them. Events like this show that there is still room for improvement in the AV system. This paper aims to present a review of the state-of-the-art algorithms proposed to enable AV behaviour prediction systems to predict trajectories and intentions for pedestrians and vehicles. This will be achieved by using information from previous literature review papers, recent works, and results obtained using well-known datasets. |
Description: | Data availability: No data was used for the research described in the article. |
URI: | https://bura.brunel.ac.uk/handle/2438/27444 |
DOI: | https://doi.org/10.1016/j.eswa.2023.121983 |
ISSN: | 0957-4174 |
Other Identifiers: | ORCID iD: M. Nazmul Huda https://orcid.org/0000-0002-5376-881X 121983 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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File | Description | Size | Format | |
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FullText.pdf | Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). | 1.78 MB | Adobe PDF | View/Open |
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