Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios
As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios. However, ego-lane detection in the missing feature scenarios is still an unsolved problem. To address this problem, previous method...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9110871/ |
| _version_ | 1818910257656627200 |
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| author | Xiaoliang Wang Yeqiang Qian Chunxiang Wang Ming Yang |
| author_facet | Xiaoliang Wang Yeqiang Qian Chunxiang Wang Ming Yang |
| author_sort | Xiaoliang Wang |
| collection | DOAJ |
| description | As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios. However, ego-lane detection in the missing feature scenarios is still an unsolved problem. To address this problem, previous methods have been devoted to proposing more complicated feature extraction algorithms, but they are very time-consuming and cannot deal with extreme scenarios. Different from others, this paper exploits prior knowledge contained in digital maps, which has a strong capability to enhance the performance of detection algorithms. Specifically, we employ the road shape extracted from OpenStreetMap as lane model, which is highly consistent with the real lane shape and irrelevant to lane features. In this way, only a few lane features are needed to eliminate the position error between the road shape and the real lane, and a search-based optimization algorithm is proposed. Experiments show that the proposed method can be applied to various scenarios and can run in real-time at a frequency of 20 Hz. At the same time, we evaluated the proposed method on the public KITTI Lane dataset where it achieves state-of-the-art performance. Moreover, our code will be open source after publication. |
| first_indexed | 2024-12-19T22:39:56Z |
| format | Article |
| id | doaj.art-886c0da34aa246bb8ed54c6deb779075 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T22:39:56Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-886c0da34aa246bb8ed54c6deb7790752022-12-21T20:03:05ZengIEEEIEEE Access2169-35362020-01-01810795810796810.1109/ACCESS.2020.30007779110871Map-Enhanced Ego-Lane Detection in the Missing Feature ScenariosXiaoliang Wang0https://orcid.org/0000-0002-6585-6876Yeqiang Qian1https://orcid.org/0000-0003-0831-8702Chunxiang Wang2https://orcid.org/0000-0002-6885-6740Ming Yang3https://orcid.org/0000-0002-8679-9137Department of Automation, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Automation, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Automation, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Automation, Shanghai Jiao Tong University, Shanghai, ChinaAs one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios. However, ego-lane detection in the missing feature scenarios is still an unsolved problem. To address this problem, previous methods have been devoted to proposing more complicated feature extraction algorithms, but they are very time-consuming and cannot deal with extreme scenarios. Different from others, this paper exploits prior knowledge contained in digital maps, which has a strong capability to enhance the performance of detection algorithms. Specifically, we employ the road shape extracted from OpenStreetMap as lane model, which is highly consistent with the real lane shape and irrelevant to lane features. In this way, only a few lane features are needed to eliminate the position error between the road shape and the real lane, and a search-based optimization algorithm is proposed. Experiments show that the proposed method can be applied to various scenarios and can run in real-time at a frequency of 20 Hz. At the same time, we evaluated the proposed method on the public KITTI Lane dataset where it achieves state-of-the-art performance. Moreover, our code will be open source after publication.https://ieeexplore.ieee.org/document/9110871/Ego-lane detectionmissing featureOpenStreetMapparameter estimation |
| spellingShingle | Xiaoliang Wang Yeqiang Qian Chunxiang Wang Ming Yang Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios IEEE Access Ego-lane detection missing feature OpenStreetMap parameter estimation |
| title | Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios |
| title_full | Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios |
| title_fullStr | Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios |
| title_full_unstemmed | Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios |
| title_short | Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios |
| title_sort | map enhanced ego lane detection in the missing feature scenarios |
| topic | Ego-lane detection missing feature OpenStreetMap parameter estimation |
| url | https://ieeexplore.ieee.org/document/9110871/ |
| work_keys_str_mv | AT xiaoliangwang mapenhancedegolanedetectioninthemissingfeaturescenarios AT yeqiangqian mapenhancedegolanedetectioninthemissingfeaturescenarios AT chunxiangwang mapenhancedegolanedetectioninthemissingfeaturescenarios AT mingyang mapenhancedegolanedetectioninthemissingfeaturescenarios |