The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Towards End-to-End Lane Detection: an Instance Segmentation Approach
Open accessAbstract
Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lane detection methods rely on a combination of highly-specialized, hand-crafted features and heuristics, usually followed by post-processing techniques, that are computationally expensive and prone to scalability due to road scene variations. More recent approaches leverage deep learning models, trained for pixel-wise lane segmentation, even when no markings are present in the image due to their big receptive field. Despite their advantages, these methods are limited to detecting a pre-defined, fixed number of lanes, e.g. ego-lanes, and can not cope with lane changes. In this paper, we go beyond the aforementioned limitations and propose to cast the lane detection problem as an instance segmentation problem - in which each lane forms its own instance - that can be trained end-to-end. To parametrize the segmented lane instances before fitting the lane, we further propose to apply a learned perspective transformation, conditioned on the image, in contrast to a fixed ”bird's-eye view” transformation. By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, predefined transformation. In summary, we propose a fast lane detection algorithm, running at 50 fps, which can handle a variable number of lanes and cope with lane changes. We verify our method on the tuSimple dataset and achieve competitive results.
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
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
Learning Deconvolution Network for Semantic Segmentation
Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Paszke, A., Chaurasia, A., Kim, S., & Culurciello, E. (2016) · arXiv · doi:10.48550/arxiv.1606.02147 · Cited by 1,826
Instance-aware Semantic Segmentation via Multi-task Network Cascades
Dai, J., He, K., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.04412 · Cited by 1,273
Instance-Aware Semantic Segmentation via Multi-task Network Cascades
Dai, J., He, K., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.343 · Cited by 1,244
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
Real time detection of lane markers in urban streets
Aly, M. (2008) · 2008 IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2008.4621152 · Cited by 678
Cited by
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
YOLOP: You Only Look Once for Panoptic Driving Perception
Wu, D., Liao, M.-W., Zhang, W.-T., Wang, X.-G., Bai, X., Cheng, W.-Q., & Liu, W.-Y. (2022) · Machine Intelligence Research · doi:10.1007/s11633-022-1339-y · Cited by 466
CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution
Liu, L., Chen, X., Zhu, S., & Tan, P. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00375 · Cited by 346
LATR: 3D Lane Detection from Monocular Images with Transformer
Luo, Y., Zheng, C., Yan, X., Kun, T., Zheng, C., Cui, S., & Li, Z. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00730 · Cited by 64
Adaptive Sensor Fault Detection and Isolation using Unscented Kalman Filter for Vehicle Positioning
Mori, D., Sugiura, H., & Hattori, Y. (2019) · 2019 IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2019.8917208 · Cited by 23
Metadata
- Authors
- Neven, D., Brabandere, B. D., Georgoulis, S., Proesmans, M., & Gool, L. V.
- Venue
- 2018 IEEE Intelligent Vehicles Symposium (IV)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 286-291
- ISSN
- 1931-0587 (ISSN-L)
- OpenAlex
- W2785872028
- Cited by
- 849
- References
- 36
- FWCI
- 67.60
- Type
- conference paper
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Towards End-to-End Lane Detection: an Instance Segmentation Approach. https://app.cochat.ai/papers/10.1109/ivs.2018.8500547
- The paper
- Neven, D., Brabandere, B. D., Georgoulis, S., Proesmans, M., & Gool, L. V. (2018). Towards End-to-End Lane Detection: an Instance Segmentation Approach. 2018 IEEE Intelligent Vehicles Symposium (IV). https://doi.org/10.1109/ivs.2018.8500547
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.
