Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106292
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Mechanical Engineering | - |
dc.creator | Xu, Z | - |
dc.creator | Sun, Y | - |
dc.creator | Liu, M | - |
dc.date.accessioned | 2024-05-09T00:52:31Z | - |
dc.date.available | 2024-05-09T00:52:31Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/106292 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2021 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. | en_US |
dc.rights | The following publication Z. Xu, Y. Sun and M. Liu, "Topo-Boundary: A Benchmark Dataset on Topological Road-Boundary Detection Using Aerial Images for Autonomous Driving," in IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7248-7255, Oct. 2021 is available at https://doi.org/10.1109/LRA.2021.3097512. | en_US |
dc.subject | Autonomous driving | en_US |
dc.subject | Imitation learning | en_US |
dc.subject | Large-scale dataset | en_US |
dc.subject | Road-boundary detection | en_US |
dc.title | Topo-boundary : a benchmark dataset on topological road-boundary detection using aerial images for autonomous driving | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 7248 | - |
dc.identifier.epage | 7255 | - |
dc.identifier.volume | 6 | - |
dc.identifier.issue | 4 | - |
dc.identifier.doi | 10.1109/LRA.2021.3097512 | - |
dcterms.abstract | Road-boundary detection is important for autonomous driving. It can be used to constrain autonomous vehicles running on road areas to ensure driving safety. Compared with online road-boundary detection using on-vehicle cameras/Lidars, offline detection using aerial images could alleviate the severe occlusion issue. Moreover, the offline detection results can be directly employed to annotate high-definition (HD) maps. In recent years, deep-learning technologies have been used in offline detection. But there still lacks a publicly available dataset for this task, which hinders the research progress in this area. So in this letter, we propose a new benchmark dataset, named Topo-boundary, for offline topological road-boundary detection. The dataset contains 25,295 1000×1000-sized 4-channel aerial images. Each image is provided with 8 training labels for different sub-tasks. We also design a new entropy-based metric for connectivity evaluation, which could better handle noises or outliers. We implement and evaluate 3 segmentation-based baselines and 5 graph-based baselines using the dataset. We also propose a new imitation-learning-based baseline which is enhanced from our previous work. The superiority of our enhancement is demonstrated from the comparison. The dataset and our-implemented code for the baselines are available at https://tonyxuqaq.github.io/Topo-boundary/. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE robotics and automation letters, Oct. 2021, v. 6, no. 4, p. 7248-7255 | - |
dcterms.isPartOf | IEEE robotics and automation letters | - |
dcterms.issued | 2021-10 | - |
dc.identifier.scopus | 2-s2.0-85110854972 | - |
dc.identifier.eissn | 2377-3766 | - |
dc.description.validate | 202405 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | ME-0023 | en_US |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Department of Science and Technology of Guangdong Province Fund; Zhongshan Municipal Science and Technology Bureau Fund | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 54315552 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Sun_Topo-Boundary_Benchmark_Dataset.pdf | Pre-Published version | 4.15 MB | Adobe PDF | View/Open |
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