The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
A Novel Cognitively Inspired Deep Learning Approach to Detect Drivable Areas for Self-driving Cars
Overview
Integrity signals
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Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Route planning in ICV systems aims to guide the efficient vehicular movement between starting and ending points, and its complexity rises in multi-destination scenarios [84].”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [84]
Citations
Cites
A Threshold Selection Method from Gray-Level Histograms
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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
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1993.341010 · Cited by 14,265
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
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1 year, 1000 km: The Oxford RobotCar dataset
Maddern, W., Pascoe, G., Linegar, C., & Newman, P. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364916679498 · Cited by 1,632 · cited 1×
“Figure 1 displays three examples of driving scenes and the corresponding driv-able areas labels from the ORD dataset [6].”
Planning-oriented Autonomous Driving
Hu, Y., Yang, J., Chen, L., Li, K., Sima, C., Zhu, X., Chai, S., Du, S., Lin, T., Wang, W., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01712 · Cited by 931
Illingworth, J., & Kittler, J. (1987) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1987.4767964 · Cited by 693
Research works printed but not linked: 4
- Jiqing C, Depeng W, Teng L, Tian L, Huabin W. All-weather road drivable area segmentation method based on cyclegan. Vis Comput. 2022;39:1–17.
- Fan C, Jingbo X, Di S. Lane detection based on machine learning algorithm. TELKOMNIKA Indones J Electr Eng. 2014;12(2):1403–9.
- Li Y, Ren J, Yan Y, Liu Q, Ma P, Petrovski A, Sun H. Cbanet: An end-to-end cross band 2-d attention network for hyperspectral change detection in remote sensing. IEEE Trans Geosci Remote Sens. 2023;61:1–11.
- Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 [Preprint]. 2017. Available from: http://arxiv.org/abs/1704.04861. · arXiv
Cited by
Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy
Zhang, X., Li, J., Zhou, J., Zhang, S., Wang, J., Yuan, Y., Liu, J., & Li, J. (2025) · Automotive Innovation · doi:10.1007/s42154-024-00310-2 · Cited by 103
Metadata
- Authors
- Jiang, F., Wang, Z., & Yue, G.
- Affiliations
- University of Science and Technology of China (CN)
- Anhui Sanlian University (CN)
- Corresponding author
- Jiang, Fengling
- Yue, Guoqing
- ORCID
- Jiang, Fengling
- Venue
- Cognitive Computation
- Published
- 2023-10-31
- Volume
- 16
- Issue
- 2
- Pages
- 517-533
- ISSN
- 1866-9956 (ISSN-L)
- 1866-9964
- OpenAlex
- W4388048626
- Cited by
- 9
- References
- 43
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 2
- FWCI
- 0.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
- Locations
- Cognitive Computation (publishedVersion)
- Funders
- the Natural Science Foundation of the Education Bureau of Anhui Province (2022AH052130)
- the project of development of an autonomous vehicle management system (HXXM2022168)
- the Key Research Project of Natural Science in Higher Education of Anhui Province (2022AH051994)
- 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 › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- drivable area detection, autonomous vehicles, salient region detection, driver attention, attention mechanism, road boundary detection, vanishing point detection, DeepLabV3+, drivable area segmentation
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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Novel Cognitively Inspired Deep Learning Approach to Detect Drivable Areas for Self-driving Cars. https://app.cochat.ai/papers/10.1007/s12559-023-10215-7
- The paper
- Jiang, F., Wang, Z., & Yue, G. (2024). A Novel Cognitively Inspired Deep Learning Approach to Detect Drivable Areas for Self-driving Cars. Cognitive Computation. https://doi.org/10.1007/s12559-023-10215-7
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.
