The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
DeepLanes: End-To-End Lane Position Estimation Using Deep Neural Networks
Abstract
Camera-based lane detection algorithms are one of the key enablers for many semi-autonomous and fully-autonomous systems, ranging from lane keep assist to level-5 automated vehicles. Positioning a vehicle between lane boundaries is the core navigational aspect of a self-driving car. Even though this should be trivial, given the clarity of lane markings on most standard roadway systems, the process is typically mired with tedious pre-processing and computational effort. We present an approach to estimate lane positions directly using a deep neural network that operates on images from laterally-mounted down-facing cameras. To create a diverse training set, we present a method to generate semi-artificial images. Besides the ability to distinguish whether there is a lane-marker present or not, the network is able to estimate the position of a lane marker with sub-centimeter accuracy at an average of 100 frames/s on an embedded automotive platform, requiring no pre-or post-processing. This system can be used not only to estimate lane position for navigation, but also provide an efficient way to validate the robustness of driver-assist features which depend on lane information.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
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
Probabilistic Lane Tracking in Difficult Road Scenarios Using Stereovision
Danescu, R., & Nedevschi, S. (2009) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2009.2018328 · Cited by 127
Cited by
Planning 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
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
Line-CNN: End-to-End Traffic Line Detection With Line Proposal Unit
Li, X., Li, J., Hu, X., & Yang, J. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2890870 · Cited by 334
Machine learning for next-generation intelligent transportation systems: A survey
Yuan, T., Da Rocha Neto, W., Rothenberg, C. E., Obraczka, K., Barakat, C., & Turletti, T. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4427 · Cited by 179
Metadata
- Authors
- Gurghian, A., Koduri, T., Bailur, S. V., Carey, K. J., & Murali, V. N.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 38-45
- OpenAlex
- W2478820856
- Cited by
- 183
- References
- 5
- FWCI
- 30.73
- Type
- conference paper
- Language
- en
- Open access
- closed
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.
