The DOI matches its Crossref record, with a small difference in title or year.
Combinatorial approach for lane detection using image and LIDAR reflectance
Abstract
Recently, lane detection algorithms have played significant roles in the field of vehicle technology. While many well working algorithms have been developed, they are hard to use in complex urban environments. In this paper, we propose an efficient approach for detecting lane markings using image information and LIDAR reflectance. The proposed algorithm has three phases: ground extraction, lane detections, and combining lane information. The proposed algorithm was implemented on a real vehicle and validated in various traffic environments, including the 2014 Hyundai Autonomous Vehicle Competition (AVC).
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Combining camera and LiDAR information improves the capacity to detect lanes in difficult environmental situations.
“With the use of both images and LiDAR data, Shin et al. [158] developed a lane detection algorithm that combines camera and LiDAR information, improving the capacity to detect lanes in difficult environmental situations.”
From Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches (2024) · cites this paper as [158]
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Cites
No references could be resolved yet.
Research works printed but not linked: 8
- 10.1109/TITS.2012.2184756 · DOI
- satzoda, 2012, Ro-bust Extraction of Lane Markings Using Gradient Angle Histograms and Directional Signed Edges, Intelligent Vehicle Symposium, 754
- 10.1109/TITS.2014.2328589 · DOI
- 10.1109/IVS.2006.1689684 · DOI
- zhang, 2004, Extrinsic Calibration of a Camera and Laser Range Finder (improves camera calibration), Intelligent Robots and Systems, 2301
- 10.1109/ITSC.2011.6082840 · DOI
- zhao, 2012, A Novel Multi-lane Detection and Tracking of Lane Markings, Intelligent Vehicle Symposium, 1084
- 10.1007/s00138-011-0404-2 · DOI · Paper page
Cited by
Sorted by citations
Tahir, N. U. A., Zhang, Z., Asim, M., Chen, J., & ELAffendi, M. (2024) · Algorithms · doi:10.3390/a17030103 · Cited by 145
Metadata
- Authors
- Shin, S., Shim, I., & Kweon, I. S.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Venue
- 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)
- Publisher
- IEEE
- Published
- 2015-10
- Pages
- 485-487
- OpenAlex
- W2209089562
- Cited by
- 13
- References
- 0
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 1 · 2020: 3 · 2018: 4 · 2016: 2
- FWCI
- 2.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Ubiquitous Robots and Ambient Intelligence (publishedVersion)
- Funders
- Korea Advanced Institute of Science and Technology
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Automated Road and Building Extraction
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Keywords
- lane detection, lane markings, autonomous vehicles, urban driving
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