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Review of graph-based hazardous event detection methods for autonomous driving systems
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Xiao, Dannier, Dianati, Mehrdad, Geiger, William Goncalves and Woodman, Roger (2023) Review of graph-based hazardous event detection methods for autonomous driving systems. IEEE Transactions on Intelligent Transportation Systems, 24 (5). pp. 4697-4715. doi:10.1109/TITS.2023.3240104 ISSN 1524-9050.
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WRAP-review-graph-based-hazardous-event-detection-methods-Woodman-2023.pdf - Accepted Version - Requires a PDF viewer. Download (4Mb) | Preview |
Official URL: http://dx.doi.org/10.1109/TITS.2023.3240104
Abstract
Automated and autonomous vehicles are often required to operate in complex road environments with potential hazards that may lead to hazardous events causing injury or even death. Therefore, a reliable autonomous hazardous event detection system is a key enabler for highly autonomous vehicles (e.g., Level 4 and 5 autonomous vehicles) to operate without human supervision for significant periods of time. One promising solution to the problem is the use of graph-based methods that are powerful tools for relational reasoning. Using graphs to organise heterogeneous knowledge about the operational environment, link scene entities (e.g., road users, static objects, traffic rules) and describe how they affect each other. Due to a growing interest and opportunity presented by graph-based methods for autonomous hazardous event detection, this paper provides a comprehensive review of the state-of-the-art graph-based methods that we categorise as rule-based, probabilistic, and machine learning-driven. Additionally, we present an in-depth overview of the available datasets to facilitate hazardous event training and evaluation metrics to assess model performance. In doing so, we aim to provide a thorough overview and insight into the key research opportunities and open challenges.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (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): | Automated vehicles, Intelligent transportation systems, Vehicle-infrastructure integration, Neural networks (Computer science), Graph theory, Bayesian statistical decision theory | ||||||||
| Journal or Publication Title: | IEEE Transactions on Intelligent Transportation Systems | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 1524-9050 | ||||||||
| Official Date: | May 2023 | ||||||||
| Dates: |
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| Volume: | 24 | ||||||||
| Number: | 5 | ||||||||
| Page Range: | pp. 4697-4715 | ||||||||
| DOI: | 10.1109/TITS.2023.3240104 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2023 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: | 8 February 2023 | ||||||||
| Date of first compliant Open Access: | 8 February 2023 |
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