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A survey on 3D object detection methods for autonomous driving applications
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Arnold, Eduardo, Al-Jarrah, Omar Y., Dianati, Mehrdad, Fallah, Saber, Oxtoby, David and Mouzakitis, Alexandros (2019) A survey on 3D object detection methods for autonomous driving applications. IEEE Transactions on Intelligent Transportation Systems, 20 (10). pp. 3782-3795. doi:10.1109/TITS.2019.2892405
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WRAP-survey-3D-object-detection-methods-autonomous-driving-applications-Arnold-2019.pdf - Accepted Version - Requires a PDF viewer. Download (3767Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TITS.2019.2892405
Abstract
An autonomous vehicle (AV) requires an accurate perception of its surrounding environment to operate reliably. The perception system of an AV, which normally employs machine learning (e.g., deep learning), transforms sensory data into semantic information that enables autonomous driving. Object detection is a fundamental function of this perception system, which has been tackled by several works, most of them using 2D detection methods. However, the 2D methods do not provide depth information, which is required for driving tasks, such as path planning, collision avoidance, and so on. Alternatively, the 3D object detection methods introduce a third dimension that reveals more detailed object's size and location information. Nonetheless, the detection accuracy of such methods needs to be improved. To the best of our knowledge, this is the first survey on 3D object detection methods used for autonomous driving applications. This paper presents an overview of 3D object detection methods and prevalently used sensors and datasets in AVs. It then discusses and categorizes the recent works based on sensors modalities into monocular, point cloud-based, and fusion methods. We then summarize the results of the surveyed works and identify the research gaps and future research directions.
| Item Type: | Journal Article | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | T Technology > TL Motor vehicles. Aeronautics. Astronautics | |||||||||
| Divisions: | Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
| Library of Congress Subject Headings (LCSH): | Autonomous vehicles, Machine learning | |||||||||
| Journal or Publication Title: | IEEE Transactions on Intelligent Transportation Systems | |||||||||
| Publisher: | IEEE | |||||||||
| ISSN: | 1524-9050 | |||||||||
| Official Date: | October 2019 | |||||||||
| Dates: |
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| Date of first compliant deposit: | 26 February 2019 | |||||||||
| Volume: | 20 | |||||||||
| Number: | 10 | |||||||||
| Page Range: | pp. 3782-3795 | |||||||||
| DOI: | 10.1109/TITS.2019.2892405 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Publisher Statement: | © 2019 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: |
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