Recent progress in road and lane detection: A survey
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More recently, machine learning-based methods which either fuse maps with vision or use fully appearance-based segmentation have been used for road and lane detection.
“More recently, machine learning-based methods which either fuse maps with vision [200] or use fully appearance-based segmentation [208] have been used.”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [200]
Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
Lane Detection With Moving Vehicles in the Traffic Scenes
Cheng, H.-Y., Jeng, B.-S., Tseng, P.-T., & Fan, K.-C. (2006) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2006.883940 · Cited by 217 · see citing works
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A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i>
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889 · see citing works
“rates, but fully automated driving will require a complete, semantic understanding of road structures and the ability to detect several lanes at long ranges [195].”
BackgroundMethodPlanning and Decision-Making for Autonomous Vehicles
Schwarting, W., Alonso-Mora, J., & Rus, D. (2018) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105157 · Cited by 972 · see citing works
Perception, Planning, Control, and Coordination for Autonomous Vehicles
Pendleton, S., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y., Rus, D., & Ang, M. (2017) · Machines · doi:10.3390/machines5010006 · Cited by 657 · see citing works
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners
Hecker, S., Dai, D., & Van Gool, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_27 · Cited by 191 · see citing works
Hata, A., & Wolf, D. (2014) · 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2014.6957753 · Cited by 150 · see citing works
Vision-Based Driver Assistance Systems: Survey, Taxonomy and Advances
Horgan, J., Hughes, C., McDonald, J., & Yogamani, S. (2015) · 2015 IEEE 18th International Conference on Intelligent Transportation Systems · doi:10.1109/itsc.2015.329 · Cited by 72 · see citing works
- Authors
- Bar Hillel, A., Lerner, R., Levi, D., & Raz, G.
- Affiliations
- General Motors (United States) (US)
- Venue
- Machine Vision and Applications
- Published
- 2012-02-07
- Volume
- 25
- Issue
- 3
- Pages
- 727-745
- ISSN
- 0932-8092 (ISSN-L)
- 1432-1769
- OpenAlex
- W2159132531
- Cited by
- 775
- References
- 2
- Citations by year
- 2026: 22 · 2025: 33 · 2024: 41 · 2023: 55 · 2022: 49 · 2021: 74 · 2020: 77 · 2019: 91 · 2018: 94 · 2017: 70 · 2016: 52 · 2015: 55 · 2014: 40 · 2013: 18 · 2012: 4
- FWCI
- 64.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Vision and Applications (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Ocean Engineering › Automated Road and Building Extraction
- Keywords
- road detection, lane detection, driver assistance systems, vehicle perception, sensing modalities, functional building blocks, research gaps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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