The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Robust Lane Detection and Tracking in Challenging Scenarios
Open accessAbstract
A lane-detection system is an important component of many intelligent transportation systems. We present a robust lane-detection-and-tracking algorithm to deal with challenging scenarios such as a lane curvature, worn lane markings, lane changes, and emerging, ending, merging, and splitting lanes. We first present a comparative study to find a good real-time lane-marking classifier. Once detection is done, the lane markings are grouped into lane-boundary hypotheses. We group left and right lane boundaries separately to effectively handle merging and splitting lanes. A fast and robust algorithm, based on random-sample consensus and particle filtering, is proposed to generate a large number of hypotheses in real time. The generated hypotheses are evaluated and grouped based on a probabilistic framework. The suggested framework effectively combines a likelihood-based object-recognition algorithm with a Markov-style process (tracking) and can also be applied to general-part-based object-tracking problems. An experimental result on local streets and highways shows that the suggested algorithm is very reliable.
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Lane detection and tracking using B-Snake
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Cited by
Data-Driven Intelligent Transportation Systems: A Survey
Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., & Chen, C. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2158001 · Cited by 1,817
Towards End-to-End Lane Detection: an Instance Segmentation Approach
Neven, D., Brabandere, B. D., Georgoulis, S., Proesmans, M., & Gool, L. V. (2018) · 2018 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2018.8500547 · Cited by 849
Recent progress in road and lane detection: A survey
Bar Hillel, A., Lerner, R., Levi, D., & Raz, G. (2014) · Machine Vision and Applications · doi:10.1007/s00138-011-0404-2 · Cited by 775
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
Ultra Fast Structure-Aware Deep Lane Detection
Qin, Z., Wang, H., & Li, X. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58586-0_17 · Cited by 554
Metadata
- Authors
- Kim, Z.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2008-03
- Volume
- 9
- Issue
- 1
- Pages
- 16-26
- ISSN
- 1524-9050 (ISSN-L)
- OpenAlex
- W2146575011
- Cited by
- 586
- References
- 16
- FWCI
- 56.67
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
