Zhu, H., Yuen, K.-V., Mihaylova, L. orcid.org/0000-0001-5856-2223 et al. (1 more author) (2017) Overview of Environment Perception for Intelligent Vehicles. IEEE Transactions on Intelligent Transportation Systems, 18 (10). pp. 2584-2601. ISSN 1524-9050
Abstract
This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Intelligent vehicles; environment perception and modeling; lane and road detection; traffic sign recognition; vehicle tracking and behavior analysis; scene understanding |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number ROYAL SOCIETY IE150823 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Feb 2017 10:41 |
Last Modified: | 13 Jul 2023 14:14 |
Published Version: | https://doi.org/10.1109/TITS.2017.2658662 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TITS.2017.2658662 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:111149 |